From 967f7c37001550b2cf4cdf880721e7162617250d Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:09:21 -0700
Subject: [PATCH 01/26] Bump version to 0.1.0b1 for beta release

---
 .readthedocs.yaml | 17 +++++++++++++++++
 setup.cfg         |  2 +-
 2 files changed, 18 insertions(+), 1 deletion(-)
 create mode 100644 .readthedocs.yaml

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
new file mode 100644
index 0000000..44023f7
--- /dev/null
+++ b/.readthedocs.yaml
@@ -0,0 +1,17 @@
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+build:
+  os: ubuntu-24.04
+  tools:
+    python: "3.10"
+
+sphinx:
+  configuration: docs/conf.py
+
+# python:
+#   install:
+#     - requirements: docs/requirements.txt
diff --git a/setup.cfg b/setup.cfg
index d341fef..a7ddc93 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -19,7 +19,7 @@ project_urls =
 [options]
 packages = find:
 include_package_data = True
-python_requires = >=3.8
+python_requires = >=3.9
 install_requires =
     PyYAML>=5.4
     pandas>=1.0

From b608dbc8990adfea68fb3fc75d585e567a91cf53 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:23:48 -0700
Subject: [PATCH 02/26] Add documentation dependencies and update RTD
 configuration

---
 .readthedocs.yaml            | 8 ++++----
 docs/source/requirements.txt | 4 ++++
 2 files changed, 8 insertions(+), 4 deletions(-)
 create mode 100644 docs/source/requirements.txt

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 44023f7..19b51a5 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -10,8 +10,8 @@ build:
     python: "3.10"
 
 sphinx:
-  configuration: docs/conf.py
+  configuration: docs/source/conf.py
 
-# python:
-#   install:
-#     - requirements: docs/requirements.txt
+python:
+  install:
+    - requirements: docs/requirements.txt
diff --git a/docs/source/requirements.txt b/docs/source/requirements.txt
new file mode 100644
index 0000000..185eda7
--- /dev/null
+++ b/docs/source/requirements.txt
@@ -0,0 +1,4 @@
+sphinx
+sphinx_rtd_theme
+sphinx-autosummary-accessors
+sphinxcontrib-napoleon
\ No newline at end of file

From d320fe26446bbf9bcd50ad2c1306fa9d30f8e01a Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:28:14 -0700
Subject: [PATCH 03/26] Add documentation dependencies

---
 docs/{source => }/requirements.txt | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/{source => }/requirements.txt (100%)

diff --git a/docs/source/requirements.txt b/docs/requirements.txt
similarity index 100%
rename from docs/source/requirements.txt
rename to docs/requirements.txt

From c97c84e6b4ec1b7ed00e16bc46838a4c6834bb17 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:33:27 -0700
Subject: [PATCH 04/26] Configure RTD: install package and dependencies for
 docs build

---
 .readthedocs.yaml     | 1 +
 docs/requirements.txt | 6 +++++-
 requirements.txt      | 4 ++--
 setup.cfg             | 2 ++
 4 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 19b51a5..21fbda8 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -15,3 +15,4 @@ sphinx:
 python:
   install:
     - requirements: docs/requirements.txt
+    - .
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 185eda7..c1a60ab 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,4 +1,8 @@
 sphinx
 sphinx_rtd_theme
 sphinx-autosummary-accessors
-sphinxcontrib-napoleon
\ No newline at end of file
+sphinxcontrib-napoleon
+
+# Dependencies
+torch
+torch_geometric
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index a2b124c..21a7bbd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,5 +14,5 @@ pyvis
 leidenalg
 dtt
 pyreadr
-# pip install torch
-# pip install torch_geometric
+torch
+torch_geometric
diff --git a/setup.cfg b/setup.cfg
index a7ddc93..e5c432e 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -51,6 +51,8 @@ dev =
     tox
     setuptools>=42
     twine
+    torch
+    torch_geometric
 
 [options.packages.find]
 where = .

From 53517c9010b91a606c14869825325cf6306f385b Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:42:40 -0700
Subject: [PATCH 05/26] Mock torch and torch_geometric to fix RTD build import
 errors

---
 docs/requirements.txt | 4 ----
 docs/source/conf.py   | 2 ++
 requirements.txt      | 4 ++--
 3 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index c1a60ab..4914502 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -2,7 +2,3 @@ sphinx
 sphinx_rtd_theme
 sphinx-autosummary-accessors
 sphinxcontrib-napoleon
-
-# Dependencies
-torch
-torch_geometric
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 6e315c0..a08fee2 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -37,3 +37,5 @@
     "matplotlib": ("https://matplotlib.org/stable/", None),
     "networkx": ("https://networkx.org/documentation/stable/", None),
 }
+
+autodoc_mock_imports = ["torch", "torch_geometric"]
diff --git a/requirements.txt b/requirements.txt
index 21a7bbd..995232c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,5 +14,5 @@ pyvis
 leidenalg
 dtt
 pyreadr
-torch
-torch_geometric
+# torch
+# torch_geometric

From 5e6b41276fa1594fb3cdfdd0cbe6145915dfaf54 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:43:38 -0700
Subject: [PATCH 06/26] syntax error

---
 .readthedocs.yaml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 21fbda8..19b51a5 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -15,4 +15,3 @@ sphinx:
 python:
   install:
     - requirements: docs/requirements.txt
-    - .

From fb3752a41ba3ab1339d68e54da7744e68616a4da Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:45:38 -0700
Subject: [PATCH 07/26] syntax error again

---
 .readthedocs.yaml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 19b51a5..28dae9d 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -15,3 +15,5 @@ sphinx:
 python:
   install:
     - requirements: docs/requirements.txt
+    - method: pip
+      path: .

From dedf0a19afc52605c3bb39233a51cef0ed7db0de Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:55:27 -0700
Subject: [PATCH 08/26] Remove workflow for docs

---
 .github/workflows/docs.yml | 47 --------------------------------------
 .gitignore                 |  1 +
 2 files changed, 1 insertion(+), 47 deletions(-)
 delete mode 100644 .github/workflows/docs.yml

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
deleted file mode 100644
index f0fd165..0000000
--- a/.github/workflows/docs.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-name: Build and Deploy Documentation
-
-on:
-  push:
-    branches: [main]
-  pull_request:
-    branches: [main]
-
-jobs:
-  build-deploy-docs:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-          check-latest: true
-
-      - name: Install Dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt
-          pip install -r requirements-dev.txt
-          pip install torch
-          pip install torch_geometric
-        shell: bash
-
-      - name: Verify Documentation Directory
-        run: test -d docs/source/ || { echo "Docs source directory missing"; exit 1; }
-
-      - name: Build Documentation
-        run: |
-          mkdir -p docs/build/html
-          sphinx-build -b html docs/source/ docs/build/html/
-        shell: bash
-
-      - name: Deploy to GitHub Pages
-        uses: peaceiris/actions-gh-pages@v3
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          publish_dir: ./docs/build/html
diff --git a/.gitignore b/.gitignore
index 266b6c2..b41f980 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,6 +19,7 @@ bioneuralnet.egg-info
 # Block sensitive file types globally
 *.csv
 *.RData
+dpmon_output/
 
 
 # Sphinx documentation build

From 4546c882fa3ce7bef4338765022f6a21615ab059 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 20 Jan 2025 17:59:29 -0700
Subject: [PATCH 09/26] Added pre commit install to README instructions

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index f3eb75c..57d76f2 100644
--- a/README.md
+++ b/README.md
@@ -117,6 +117,7 @@ If you plan to contribute to BioNeuralNet:
 git clone https://github.com/UCD-BDLab/BioNeuralNet.git
 cd BioNeuralNet
 pip install -r requirements-dev.txt
+pre-commit install
 pytest
 ```
 

From cbb391ea4b58d2141e9400048584d524d0be8c1d Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 23 Jan 2025 10:42:28 -0700
Subject: [PATCH 10/26] Simplified subject representation and docs

subject_representation.py separates the embedding generation entirely to gnns code.
documentation has been updated to reflect these changes
also cleaned the index file
---
 README.md                                     |  2 +-
 bioneuralnet/downstream_task/dpmon.py         | 42 +++++-----
 .../network_embedding/gnn_embedding.py        |  2 +-
 .../subject_representation.py                 | 78 ++++--------------
 docs/source/index.rst                         | 30 ++-----
 docs/source/tutorials/example_1.rst           | 79 +++++++++++++++----
 docs/source/tutorials/example_2.rst           | 49 +++++++++---
 7 files changed, 144 insertions(+), 138 deletions(-)

diff --git a/README.md b/README.md
index 57d76f2..82ea8e7 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 ![GitHub Issues](https://img.shields.io/github/issues/UCD-BDLab/BioNeuralNet)
 ![GitHub Contributors](https://img.shields.io/github/contributors/UCD-BDLab/BioNeuralNet)
 
-## Welcome to BioNeuralNet Beta 0.1
+## Welcome to [BioNeuralNet Beta 0.1](https://bioneuralnet.readthedocs.io/en/latest/index.html)
 
 ![BioNeuralNet Logo](/assets/LOGO_WB.png)
 
diff --git a/bioneuralnet/downstream_task/dpmon.py b/bioneuralnet/downstream_task/dpmon.py
index 37de90f..d375782 100644
--- a/bioneuralnet/downstream_task/dpmon.py
+++ b/bioneuralnet/downstream_task/dpmon.py
@@ -33,7 +33,7 @@ def __init__(
         omics_list: List[pd.DataFrame],
         phenotype_data: pd.DataFrame,
         clinical_data: pd.DataFrame,
-        model: str = "GCN",
+        model: str = "GAT",
         gnn_hidden_dim: int = 16,
         layer_num: int = 5,
         nn_hidden_dim1: int = 8,
@@ -55,10 +55,10 @@ def __init__(
         if (
             phenotype_data is None
             or phenotype_data.empty
-            or "finalgold_visit" not in phenotype_data.columns
+            or "phenotype" not in phenotype_data.columns
         ):
             raise ValueError(
-                "Phenotype data must contain 'finalgold_visit' and cannot be empty."
+                "Phenotype data must contain 'phenotype' and cannot be empty."
             )
 
         if not omics_list or any(df.empty for df in omics_list):
@@ -84,7 +84,6 @@ def __init__(
         self.gpu = gpu
         self.cuda = cuda
         self.output_dir = output_dir if output_dir else f"dpmon_output"
-        # f"dpmon_output_{os.getpid()}" for unique output directory
 
         os.makedirs(self.output_dir, exist_ok=True)
         logger.info("Initialized DPMON with the provided parameters.")
@@ -96,7 +95,7 @@ def run(self) -> pd.DataFrame:
         **Steps:**
 
         1. **Combining Omics and Phenotype Data**:
-           - Merges the provided omics datasets and ensures that the phenotype (`finalgold_visit`) column is included.
+           - Merges the provided omics datasets and ensures that the phenotype (`phenotype`) column is included.
 
         2. **Tuning or Training**:
            - **Tuning**: If `tune=True`, performs hyperparameter tuning using Ray Tune and returns an empty DataFrame.
@@ -145,10 +144,11 @@ def run(self) -> pd.DataFrame:
             "tune": self.tune,
         }
 
+        # Combine omics datasets
         combined_omics = pd.concat(self.omics_list, axis=1)
-        if "finalgold_visit" not in combined_omics.columns:
+        if "phenotype" not in combined_omics.columns:
             combined_omics = combined_omics.merge(
-                self.phenotype_data[["finalgold_visit"]],
+                self.phenotype_data[["phenotype"]],
                 left_index=True,
                 right_index=True,
             )
@@ -208,7 +208,7 @@ def slice_omics_datasets(
         logger.error(f"Nodes missing in omics data: {missing_nodes}")
         raise ValueError("Missing nodes in omics dataset.")
 
-    selected_columns = omics_network_nodes_names + ["finalgold_visit"]
+    selected_columns = omics_network_nodes_names + ["phenotype"]
     return [omics_dataset[selected_columns]]
 
 
@@ -288,10 +288,10 @@ def run_standard_training(
                 gnn_hidden_dim=dpmon_params["gnn_hidden_dim"],
                 gnn_layer_num=dpmon_params["layer_num"],
                 ae_encoding_dim=1,
-                nn_input_dim=omics_data.drop(["finalgold_visit"], axis=1).shape[1],
+                nn_input_dim=omics_data.drop(["phenotype"], axis=1).shape[1],
                 nn_hidden_dim1=dpmon_params["nn_hidden_dim1"],
                 nn_hidden_dim2=dpmon_params["nn_hidden_dim2"],
-                nn_output_dim=omics_data["finalgold_visit"].nunique(),
+                nn_output_dim=omics_data["phenotype"].nunique(),
             ).to(device)
 
             criterion = nn.CrossEntropyLoss()
@@ -302,12 +302,12 @@ def run_standard_training(
             )
 
             train_features = torch.FloatTensor(
-                omics_data.drop(["finalgold_visit"], axis=1).values
+                omics_data.drop(["phenotype"], axis=1).values
             ).to(device)
             train_labels = {
-                "labels": torch.LongTensor(
-                    omics_data["finalgold_visit"].values.copy()
-                ).to(device),
+                "labels": torch.LongTensor(omics_data["phenotype"].values.copy()).to(
+                    device
+                ),
                 "omics_network": omics_network.to(device),
             }
 
@@ -333,7 +333,7 @@ def run_standard_training(
                 )
                 predictions_df = pd.DataFrame(
                     {
-                        "Actual": omics_data["finalgold_visit"].values,
+                        "Actual": omics_data["phenotype"].values,
                         "Predicted": predicted.cpu().numpy(),
                     }
                 )
@@ -391,10 +391,10 @@ def tune_train_n(config):
                 gnn_hidden_dim=config["gnn_hidden_dim"],
                 gnn_layer_num=config["gnn_layer_num"],
                 ae_encoding_dim=1,
-                nn_input_dim=omics_data.drop(["finalgold_visit"], axis=1).shape[1],
+                nn_input_dim=omics_data.drop(["phenotype"], axis=1).shape[1],
                 nn_hidden_dim1=config["nn_hidden_dim1"],
                 nn_hidden_dim2=config["nn_hidden_dim2"],
-                nn_output_dim=omics_data["finalgold_visit"].nunique(),
+                nn_output_dim=omics_data["phenotype"].nunique(),
             ).to(device)
 
             criterion = nn.CrossEntropyLoss()
@@ -403,12 +403,12 @@ def tune_train_n(config):
             )
 
             train_features = torch.FloatTensor(
-                omics_data.drop(["finalgold_visit"], axis=1).values
+                omics_data.drop(["phenotype"], axis=1).values
             ).to(device)
             train_labels = {
-                "labels": torch.LongTensor(
-                    omics_data["finalgold_visit"].values.copy()
-                ).to(device),
+                "labels": torch.LongTensor(omics_data["phenotype"].values.copy()).to(
+                    device
+                ),
                 "omics_network": omics_network_tg.to(device),
             }
 
diff --git a/bioneuralnet/network_embedding/gnn_embedding.py b/bioneuralnet/network_embedding/gnn_embedding.py
index 82a0212..f32d84b 100644
--- a/bioneuralnet/network_embedding/gnn_embedding.py
+++ b/bioneuralnet/network_embedding/gnn_embedding.py
@@ -29,7 +29,7 @@ def __init__(
         omics_data: pd.DataFrame,
         phenotype_data: pd.DataFrame,
         clinical_data: pd.DataFrame,
-        phenotype_col: str = "finalgold_visit",
+        phenotype_col: str = "phenotype",
         model_type: str = "GAT",
         hidden_dim: int = 64,
         layer_num: int = 2,
diff --git a/bioneuralnet/subject_representation/subject_representation.py b/bioneuralnet/subject_representation/subject_representation.py
index 6b5ed39..c2142cd 100644
--- a/bioneuralnet/subject_representation/subject_representation.py
+++ b/bioneuralnet/subject_representation/subject_representation.py
@@ -21,15 +21,7 @@ def __init__(
         phenotype_data: pd.DataFrame,
         clinical_data: pd.DataFrame,
         embeddings: pd.DataFrame = None,
-        model_type: str = "GCN",
-        phenotype_col: str = "DiseaseStage",
-        hidden_dim: int = 64,
-        layer_num: int = 2,
-        dropout: bool = True,
-        num_epochs: int = 100,
-        lr: float = 1e-3,
-        weight_decay: float = 1e-4,
-        gpu: bool = False,
+        reduce_method: str = "PCA",
     ):
         """
         Parameters
@@ -44,24 +36,6 @@ def __init__(
             (samples x clinical_vars) table.
         embeddings : pd.DataFrame, optional
             Precomputed node embeddings to skip GNN if not None or empty.
-        model_type : str, optional
-            GNN model type ("GCN", "GAT", "SAGE", "GIN").
-        phenotype_col : str, optional
-            Column in phenotype_data for correlation-based node label. Defaults to "DiseaseStage".
-        hidden_dim : int, optional
-            The GNN hidden dimension. Defaults to 64.
-        layer_num : int, optional
-            Number of GNN layers. Defaults to 2.
-        dropout : bool, optional
-            Whether to apply dropout in GNN. Defaults to True.
-        num_epochs : int, optional
-            Number of epochs if we train the GNN. Defaults to 100.
-        lr : float, optional
-            Learning rate. Defaults to 1e-3.
-        weight_decay : float, optional
-            L2 weight decay. Defaults to 1e-4.
-        gpu : bool, optional
-            Whether to use GPU if available. Defaults to False.
         """
         if adjacency_matrix is None or adjacency_matrix.empty:
             raise ValueError("Adjacency matrix is required and cannot be empty.")
@@ -75,7 +49,7 @@ def __init__(
         if embeddings is None or embeddings.empty:
             self.logger = get_logger(__name__)
             self.logger.info(
-                "No precomputed embeddings; defaulting to GNN-based approach."
+                "No precomputed embeddings -> defaulting to GNN-based approach."
             )
             self.embedding_method = "GNNs"
         else:
@@ -86,16 +60,7 @@ def __init__(
         self.phenotype_data = phenotype_data
         self.clinical_data = clinical_data
         self.embeddings = embeddings
-
-        self.model_type = model_type
-        self.phenotype_col = phenotype_col
-        self.hidden_dim = hidden_dim
-        self.layer_num = layer_num
-        self.dropout = dropout
-        self.num_epochs = num_epochs
-        self.lr = lr
-        self.weight_decay = weight_decay
-        self.gpu = gpu
+        self.reduce_method = reduce_method
 
         self.logger = get_logger(__name__)
         self.logger.info("Initialized GraphEmbedding with direct data inputs.")
@@ -152,24 +117,13 @@ def generate_embeddings(self) -> pd.DataFrame:
                 raise ValueError("Provided embeddings are empty.")
             return self.embeddings
 
-        # Otherwise, we do a GNN-based approach
-        from ..network_embedding import GNNEmbedding
-
-        gnn_embedder = GNNEmbedding(
-            adjacency_matrix=self.adjacency_matrix,
-            omics_data=self.omics_data,
-            phenotype_data=self.phenotype_data,
-            clinical_data=self.clinical_data,
-            phenotype_col=self.phenotype_col,
-            model_type=self.model_type,
-            hidden_dim=self.hidden_dim,
-            layer_num=self.layer_num,
-            dropout=self.dropout,
-            num_epochs=self.num_epochs,
-            lr=self.lr,
-            weight_decay=self.weight_decay,
-            gpu=self.gpu,
-        )
+        else:
+            gnn_embedder = GNNEmbedding(
+                adjacency_matrix=self.adjacency_matrix,
+                omics_data=self.omics_data,
+                phenotype_data=self.phenotype_data,
+                clinical_data=self.clinical_data,
+            )
 
         embeddings_dict = gnn_embedder.run()
         embeddings_tensor = embeddings_dict["graph"]
@@ -178,9 +132,7 @@ def generate_embeddings(self) -> pd.DataFrame:
         embeddings_df = pd.DataFrame(embeddings_tensor.numpy(), index=node_names)
         return embeddings_df
 
-    def reduce_embeddings(
-        self, embeddings: pd.DataFrame, method: str = "pca"
-    ) -> pd.Series:
+    def reduce_embeddings(self, embeddings: pd.DataFrame) -> pd.Series:
         """
         Reduce embeddings to a single dimension per node using specified method.
 
@@ -204,21 +156,21 @@ def reduce_embeddings(
         if embeddings.empty:
             raise ValueError("Embeddings DataFrame is empty.")
 
-        if method == "pca":
+        if self.reduce_method == "PCA":
             self.logger.info("Reducing node embeddings to 1D via PCA.")
             pca = PCA(n_components=1)
             principal_components = pca.fit_transform(embeddings)
             reduced_embedding = pd.Series(
                 principal_components.flatten(), index=embeddings.index, name="PC1"
             )
-        elif method == "average":
+        elif self.reduce_method == "AVG":
             self.logger.info("Reducing node embeddings to 1D via averaging.")
             reduced_embedding = embeddings.mean(axis=1)
-        elif method == "maximum":
+        elif self.reduce_method == "MAX":
             self.logger.info("Reducing node embeddings to 1D via maximum.")
             reduced_embedding = embeddings.max(axis=1)
         else:
-            raise ValueError(f"Unsupported reduction method: {method}")
+            raise ValueError(f"Unsupported reduction method: {self.reduce_method}")
 
         return reduced_embedding
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 8dbad23..2953f3b 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -42,7 +42,7 @@ For additional installation details, including GPU usage for GNNs, see :doc:`ins
 **Example: Transforming Multi-Omics for Enhanced Disease Prediction**
 ---------------------------------------------------------------------
 
-`View full-size image: Transforming Multi-Omics for Enhanced Disease Prediction <https://ramosv.github.io/_images/Overview.png>`_
+`View full-size image: Transforming Multi-Omics for Enhanced Disease Prediction <https://bioneuralnet.readthedocs.io/en/latest/_images/Overview.png>`_
 
 .. figure:: _static/Overview.png
    :align: center
@@ -116,6 +116,8 @@ Below is a quick example demonstrating the following steps:
   - **Adjacency Matrix**: The multi-omics network representation.
   - **Predictions**: Disease phenotype predictions for each sample.
 
+**Note**: For a complete script, see `examples/example_2.py` in the repository.
+
 **BioNeuralNet Overview: Multi-Omics Integration with Graph Neural Networks**
 -----------------------------------------------------------------------------
 
@@ -139,12 +141,12 @@ BioNeuralNet offers five core steps in a typical workflow:
 4. **Subject Representation**:
 
    - Integrate node embeddings back into omics data to enrich each subject’s feature vector by weighting columns with the learned embedding scalars.
-
+   - For a complete example, see `examples/example_1.py` in the repository.
 5. **Downstream Tasks**:
 
    - Perform advanced analyses such as disease prediction via **DPMON**, which trains a GNN end-to-end alongside a classifier, incorporating both local and global network information.
 
-`View full-size image: BioNeuralNet Overview <https://ramosv.github.io/_images/BioNeuralNet.png>`_
+`View full-size image: BioNeuralNet Overview <https://bioneuralnet.readthedocs.io/en/latest/_images/BioNeuralNet.png>`_
 
 .. figure:: _static/BioNeuralNet.png
    :align: center
@@ -152,28 +154,6 @@ BioNeuralNet offers five core steps in a typical workflow:
 
    BioNeuralNet Overview
 
-**Subject Representation**
-
-Subject representation integrates node embeddings into omics data, enriching phenotypic and clinical context. Below is an example workflow:
-
-`View full-size image: Subject Representation <https://ramosv.github.io/_images/SubjectRepresentation.png>`_
-
-.. figure:: _static/SubjectRepresentation.png
-   :align: center
-   :alt: Subject Representation Workflow
-
-   Subject-level embeddings provide richer phenotypic and clinical context.
-
-**Disease Prediction**
-
-`View full-size image: Disease Prediction (DPMON) <https://ramosv.github.io/_images/DPMON.png>`_
-
-.. figure:: _static/DPMON.png
-   :align: center
-   :alt: Disease Prediction (DPMON)
-
-   Embedding-enhanced subject data using DPMON for improved disease prediction.
-
 Documentation Overview
 ----------------------
 
diff --git a/docs/source/tutorials/example_1.rst b/docs/source/tutorials/example_1.rst
index 1f1894d..5962691 100644
--- a/docs/source/tutorials/example_1.rst
+++ b/docs/source/tutorials/example_1.rst
@@ -1,11 +1,29 @@
 Example 1: SmCCNet + GNN Embeddings + Subject Representation
 ============================================================
+This tutorial illustrates how to:
 
-This tutorial shows how to:
+1. **Build:** an adjacency matrix with SmCCNet (external).
+2. **Enhance Representation:** Enhance the representation of subjects using GNN embeddings.
+
+**Workflow**:
+
+1. **Construct**:
+   - A multi-omics network adjacency using SmCCNet (an external R-based tool).
+2. **Generate**:
+   - Node embeddings with a Graph Neural Network (GNN).
+3. **Integrate** :
+   - Those embeddings into subject-level omics data for enhanced representation.
+4. **Diagram of the workflow**:
+   - The figure below illustrates the process.
+
+`View full-size image: Subject Representation <https://bioneuralnet.readthedocs.io/en/latest/_images/SubjectRepresentation.png>`_
+
+.. figure:: _static/SubjectRepresentation.png
+   :align: center
+   :alt: Subject Representation Workflow
+
+   Subject-level embeddings provide richer phenotypic and clinical context.
 
-1. **Construct** a multi-omics network adjacency using SmCCNet (an external R-based tool).
-2. **Generate** node embeddings with a Graph Neural Network (GNN).
-3. **Integrate** those embeddings into subject-level omics data for enhanced representation.
 
 **Step-by-Step**:
 
@@ -25,22 +43,30 @@ This tutorial shows how to:
 .. note::
    For a **complete** script, see `examples/example_1.py` in the repository.
 
-Below is a **partial** snippet:
+Below is a **complete** snippet:
 
 .. code-block:: python
 
+   import pandas as pd
    from bioneuralnet.external_tools import SmCCNet
    from bioneuralnet.network_embedding import GNNEmbedding
    from bioneuralnet.subject_representation import GraphEmbedding
 
    # 1) Prepare data
-   omics_data = ...
-   phenotype_data = ...
-   clinical_data = ...
+   omics_data = pd.read_csv("data/omics_data.csv")
+   phenotype_data = pd.read_csv("data/phenotype_data.csv")
+   clinical_data = pd.read_csv("data/clinical_data.csv")
 
    # 2) Run SmCCNet to get adjacency
-   smcc = SmCCNet(phenotype_data=phenotype_data, omics_data=omics_data)
-   adjacency_matrix = smcc.run()
+   smccnet = SmCCNet(
+      phenotype_df=phenotype_data,
+      omics_df=omics_data,
+      data_types=["genes, proteins"]
+      kfolds=5,
+      summarization = "NetSHy",
+      seed: 127,
+      )
+   adjacency_matrix = smccnet.run()
 
    # 3) Generate embeddings
    gnn = GNNEmbedding(
@@ -48,23 +74,42 @@ Below is a **partial** snippet:
        omics_data=omics_data,
        phenotype_data=phenotype_data,
        clinical_data=clinical_data,
-       model_type='GCN'
+       phenotype_col = "phenotype",
+       model_type='GAT',
+       hidden_dim=32,
+       layer_num = 4,
+       dropout=True,
+       num_epochs=20,
+       lr=1e-3,
+       weight_decay=1e-4,
    )
    embedding_dict = gnn.run()
+
+   # Node embeddings tensor
    node_embeddings = embedding_dict["graph"]
 
-   # 4) Subject-level representation
+   node_name = adjacency_matrix.columns.index
+   node_embeddings_df = pd.DataFrame(node_embeddings.numpy(), index=node_name)
+
+   # 4) Subject-level representation:
    graph_embed = GraphEmbedding(
        adjacency_matrix=adjacency_matrix,
        omics_data=omics_data,
        phenotype_data=phenotype_data,
-       clinical_data=clinical_data
+       clinical_data=clinical_data,
+       embeddings = node_embeddings_df,
+       reduce_method = "PCA"
    )
+   #Other options to reduce embeddings inlcude "PCA", "avg" and "max".
    enhanced_data = graph_embed.run()
-
    print("Enhanced omics data shape:", enhanced_data.shape)
+   print("Enhanced omics data columns:", enhanced_data.head())
+
+   #saving your enhanced data to a csv file
+   enhanced_data.to_csv("enhanced_omics_data.csv")
 
 **Results**:
-- **Adjacency Matrix** from SmCCNet
-- **Node Embeddings** from GNN
-- **Enhanced Omics Data**, integrating node embeddings for subject-level analysis
+
+   - **Adjacency Matrix** from SmCCNet
+   - **Node Embeddings** from GNN
+   - **Enhanced Omics Data**, integrating node embeddings for subject-level analysis
diff --git a/docs/source/tutorials/example_2.rst b/docs/source/tutorials/example_2.rst
index d43e446..86a9b29 100644
--- a/docs/source/tutorials/example_2.rst
+++ b/docs/source/tutorials/example_2.rst
@@ -17,26 +17,55 @@ This tutorial illustrates how to:
 3. **Disease Prediction**:
    - `DPMON` integrates the adjacency matrix, omics data, and phenotype to train a GNN + classifier end-to-end.
 
+4. **Diagram of the workflow**: The figure below illustrates the process.
+
+`View full-size image: Disease Prediction (DPMON) <https://bioneuralnet.readthedocs.io/en/latest/_images/DPMON.png>`_
+
+.. figure:: _static/DPMON.png
+   :align: center
+   :alt: Disease Prediction (DPMON)
+
+   Embedding-enhanced subject data using DPMON for improved disease prediction.
+
 .. code-block:: python
 
    from bioneuralnet.external_tools import SmCCNet
    from bioneuralnet.downstream_task import DPMON
    import pandas as pd
 
-   # 1) Load data
-   omics_data = pd.read_csv("omics_data.csv", index_col=0)
-   phenotype_data = pd.read_csv("phenotype_data.csv", index_col=0)
+   # 1) Prepare data
+   omics_data = pd.read_csv("data/omics_data.csv")
+   phenotype_data = pd.read_csv("data/phenotype_data.csv")
+   clinical_data = pd.read_csv("data/clinical_data.csv")
 
-   # 2) Generate adjacency
-   smcc = SmCCNet(phenotype_data=phenotype_data, omics_data=omics_data)
-   adjacency_matrix = smcc.run()
+   # 2) Run SmCCNet to get adjacency
+   smccnet = SmCCNet(
+      phenotype_df=phenotype_data,
+      omics_df=omics_data,
+      data_types=["genes, proteins"]
+      kfolds=5,
+      summarization = "NetSHy",
+      seed: 127,
+      )
+   adjacency_matrix = smccnet.run()
 
    # 3) Disease Prediction with DPMON
    dpmon = DPMON(
-       adjacency_matrix=adjacency_matrix,
-       omics_list=[omics_data],
-       phenotype_data=phenotype_data,
-       model='GAT'
+      adjacency_matrix=adjacency_matrix,
+      omics_list=[omics_data],
+      phenotype_data=phenotype_data,
+      clinical_data=clinical_data,
+      model: "GAT",
+      gnn_hidden_dim: 64,
+      layer_num: 3,
+      nn_hidden_dim1: 2,
+      nn_hidden_dim2: 2,
+      epoch_num: 10,
+      repeat_num: 5,
+      lr: 0.01,
+      weight_decay: 1e-4,
+      tune: True,
+      gpu: False
    )
    predictions = dpmon.run()
    print("Disease predictions:\n", predictions)

From b23fe58a0da8bb6b2b1c25808739eb1bfb1d82a2 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 23 Jan 2025 15:53:51 -0700
Subject: [PATCH 11/26] All test passing

---
 tests/test_dpmon.py         | 2 +-
 tests/test_gnn_embedding.py | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/test_dpmon.py b/tests/test_dpmon.py
index c7381ff..fbc4730 100644
--- a/tests/test_dpmon.py
+++ b/tests/test_dpmon.py
@@ -20,7 +20,7 @@ def setUp(self):
         self.omics_data2 = pd.DataFrame({"gene3": [5, 6]}, index=["sample1", "sample2"])
 
         self.phenotype_data = pd.DataFrame(
-            {"finalgold_visit": [2, 3]}, index=["sample1", "sample2"]
+            {"phenotype": [2, 3]}, index=["sample1", "sample2"]
         )
 
         self.features_data = pd.DataFrame(
diff --git a/tests/test_gnn_embedding.py b/tests/test_gnn_embedding.py
index 713e313..ea18d02 100644
--- a/tests/test_gnn_embedding.py
+++ b/tests/test_gnn_embedding.py
@@ -28,7 +28,7 @@ def setUp(self):
         )
 
         self.phenotype_data = pd.DataFrame(
-            {"finalgold_visit": [0, 1]}, index=["sample1", "sample2"]
+            {"phenotype": [0, 1]}, index=["sample1", "sample2"]
         )
 
     @patch.object(
@@ -46,6 +46,7 @@ def test_run(self, mock_gen_emb):
             omics_data=self.omics_data,
             phenotype_data=self.phenotype_data,
             clinical_data=self.clinical_data,
+            phenotype_col="phenotype",
             model_type="GCN",
             hidden_dim=2,
             layer_num=2,

From 371f956ce898ca3a6ef33234499b0997cd549b2a Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 23 Jan 2025 16:05:16 -0700
Subject: [PATCH 12/26] downloads measurements added to index and readme

---
 README.md             | 3 ++-
 docs/source/index.rst | 2 ++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 82ea8e7..bfcff26 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,10 @@
 
 ![License](https://img.shields.io/badge/license-MIT-blue.svg)
 ![PyPI](https://img.shields.io/pypi/v/bioneuralnet)
-![Python Versions](https://img.shields.io/pypi/pyversions/bioneuralnet)
 ![GitHub Issues](https://img.shields.io/github/issues/UCD-BDLab/BioNeuralNet)
 ![GitHub Contributors](https://img.shields.io/github/contributors/UCD-BDLab/BioNeuralNet)
+![Downloads](https://static.pepy.tech/badge/bioneuralnet)
+
 
 ## Welcome to [BioNeuralNet Beta 0.1](https://bioneuralnet.readthedocs.io/en/latest/index.html)
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 2953f3b..069be73 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -9,6 +9,8 @@ Welcome to BioNeuralNet Beta 0.1
    :target: https://pypi.org/project/bioneuralnet/
 .. image:: https://img.shields.io/pypi/pyversions/bioneuralnet
    :target: https://pypi.org/project/bioneuralnet/
+.. image:: https://static.pepy.tech/badge/bioneuralnet
+   :target: https://pepy.tech/project/bioneuralnet
 
 .. figure:: _static/LOGO_WB.png
    :align: center

From 1d36af3216a4690c748f84b6b7f50c54872df5e4 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 23 Jan 2025 16:15:58 -0700
Subject: [PATCH 13/26] removed files

---
 ArunTest.py             | 129 ----------------
 scripts/fast-install.py | 332 ----------------------------------------
 2 files changed, 461 deletions(-)
 delete mode 100644 ArunTest.py
 delete mode 100644 scripts/fast-install.py

diff --git a/ArunTest.py b/ArunTest.py
deleted file mode 100644
index 84a8e75..0000000
--- a/ArunTest.py
+++ /dev/null
@@ -1,129 +0,0 @@
-"""
-Example 3: Disease Prediction Using Graph Information (SmCCNet + Disease Prediction using Multi-Omics Networks (DPMON))
-======================================================================================================================
-
-This script demonstrates a workflow where we first generate a graph using Sparse Multiple Canonical Correlation Network
-(SmCCNet), and then use that network matrix to run Disease Prediction using Multi-Omics Networks (DPMON), a pipeline
-that leverages the power of Graph Neural Networks (GNNs) specifically designed to predict disease phenotypes.
-
-Steps:
-1. Generate an adjacency matrix using SmCCNet based on multi-omics and phenotype data.
-2. Utilize DPMON to predict disease phenotypes using the network information and omics data.
-"""
-
-import pandas as pd
-from bioneuralnet.external_tools import SmCCNet
-from bioneuralnet.downstream_task import DPMON
-
-
-def run_smccnet_dpmon_workflow(
-    omics_genes: pd.DataFrame, phenotype: pd.Series, clinical_data: pd.DataFrame
-) -> pd.DataFrame:
-    """
-    Executes the hybrid workflow combining SmCCNet for network generation and DPMON for disease prediction.
-
-    This function performs the following steps:
-        1. Generates an adjacency matrix using SmCCNet.
-        2. Initializes and runs DPMON for disease prediction based on the adjacency matrix.
-        3. Returns the disease prediction results.
-
-    Args:
-        omics_proteins (pd.DataFrame): DataFrame containing protein data.
-        omics_metabolites (pd.DataFrame): DataFrame containing metabolite data.
-        phenotype_data (pd.Series): Series containing phenotype information.
-        clinical_data (pd.DataFrame): DataFrame containing clinical data.
-
-    Returns:
-        pd.DataFrame: Disease prediction results from DPMON.
-    """
-    try:
-        smccnet_instance = SmCCNet(
-            phenotype_df=phenotype,
-            omics_dfs=[omics_genes],
-            data_types=["genes"],
-            kfold=5,
-            summarization="PCA",
-            seed=732,
-        )
-        adjacency_matrix = smccnet_instance.run()
-        print("Adjacency matrix generated using SmCCNet.")
-
-        dpmon_instance = DPMON(
-            adjacency_matrix=adjacency_matrix,
-            omics_list=[omics_genes],
-            phenotype_data=phenotype,
-            clinical_data=clinical_data,
-            model="GCN",
-            tune=False,
-            gpu=False,
-        )
-
-        predictions_df = dpmon_instance.run()
-        if not predictions_df.empty:
-            print("DPMON workflow completed successfully. Predictions generated.")
-        else:
-            print(
-                "DPMON hyperparameter tuning completed. No predictions were generated."
-            )
-
-        return predictions_df
-
-    except Exception as e:
-        print(f"An error occurred during the SmCCNet + DPMON workflow: {e}")
-        raise e
-
-
-if __name__ == "__main__":
-    try:
-        print("Starting SmCCNet + DPMON Hybrid Workflow...")
-
-        omics_genes = pd.read_csv("example_data/COPDGeneCounts.csv")
-        omics_genes = omics_genes.drop(
-            [
-                "geneID",
-                "end",
-                "strand",
-                "gene_id",
-                "gene_name",
-                "gene_type",
-                "chr",
-                "start",
-            ],
-            axis=1,
-        )
-        omics_genes_t = omics_genes.T
-        omics_genes_t = omics_genes_t.reset_index().rename(columns={"index": "sid"})
-
-        new_header = omics_genes_t.iloc[0].copy()
-        new_header.iloc[0] = "sid"
-        omics_genes_t = omics_genes_t[1:]
-        omics_genes_t.columns = new_header
-        omics_genes_t = omics_genes_t.reset_index(drop=True)
-
-        # gene_names = omics_genes["geneID_split"]
-        md = pd.read_csv("example_data/COPDGeneMetadata.csv")
-        phenotype = md[["sid", "finalgold_visit"]].reset_index()
-        clinical_data = md[
-            ["sid", "age_visit", "gender", "smoking_status"]
-        ].reset_index(drop=True)
-
-        phenotype_subset = phenotype[["sid", "finalgold_visit"]]
-        phenotype_subset["finalgold_visit"] = pd.to_numeric(
-            phenotype_subset["finalgold_visit"], errors="coerce"
-        )
-
-        print(f"Gene name:\n {omics_genes_t}")
-        print(f"Phenotype subset: \n{phenotype_subset}")
-        print(f"Cclinical data: \n{clinical_data}")
-
-        predictions = run_smccnet_dpmon_workflow(
-            omics_genes_t, phenotype_subset, clinical_data
-        )
-
-        print("DPMON Predictions:")
-        print(predictions)
-
-        print("Hybrid Workflow completed successfully.\n")
-    except Exception as e:
-        print(f"An error occurred during the execution: {e}")
-        raise e
diff --git a/scripts/fast-install.py b/scripts/fast-install.py
deleted file mode 100644
index b37fa45..0000000
--- a/scripts/fast-install.py
+++ /dev/null
@@ -1,332 +0,0 @@
-# #!/usr/bin/env python3
-
-# import os
-# import platform
-# import subprocess
-# import sys
-# import shutil
-# import re
-# import argparse
-
-# def run_command(command, shell=False, check=True, env=None):
-#     try:
-#         if isinstance(command, list):
-#             cmd_str = ' '.join(command)
-#         else:
-#             cmd_str = command
-#         print(f"Running command: {cmd_str}")
-#         subprocess.run(command, shell=shell, check=check, env=env)
-#     except subprocess.CalledProcessError as e:
-#         print(f"Command failed with exit code {e.returncode}: {cmd_str}")
-#         sys.exit(e.returncode)
-#     except Exception as e:
-#         print(f"An unexpected error occurred: {e}")
-#         sys.exit(1)
-
-# def parse_arguments():
-#     parser = argparse.ArgumentParser(description="Fast Install Script for BioNeuralNet")
-#     parser.add_argument('-dev', '--dev', action='store_true', help='Install development dependencies')
-#     parser.add_argument('-cuda', '--cuda', action='store_true', help='Install CUDA-enabled PyTorch if supported')
-#     parser.add_argument('--cuda-version', type=str, choices=['11.8', '12.1', '12.4'], help='Specify CUDA version to install (supported: 11.8, 12.1, 12.4)')
-#     return parser.parse_args()
-
-# def is_command_available(command):
-#     return shutil.which(command) is not None
-
-# def detect_cuda_version():
-#     try:
-#         output = subprocess.check_output(['nvcc', '--version'], stderr=subprocess.STDOUT)
-#         output = output.decode('utf-8')
-#         match = re.search(r'release\s+(\d+\.\d+)', output)
-#         if match:
-#             return match.group(1)
-#     except (subprocess.CalledProcessError, FileNotFoundError):
-#         pass
-#     return None
-
-# def install_r_system_dependencies(os_type):
-#     print("\nInstalling system dependencies for R...")
-#     if os_type == "Linux":
-#         run_command(["sudo", "apt-get", "update"])
-#         run_command([
-#             "sudo", "apt-get", "install", "-y",
-#             "libxml2-dev",
-#             "libcurl4-openssl-dev",
-#             "libssl-dev",
-#             "libpng-dev",
-#             "r-base"
-#         ])
-#     elif os_type == "Darwin":
-#         if not is_command_available("R"):
-#             print("R is not installed. Installing R via Homebrew...")
-#             if not is_command_available("brew"):
-#                 print("Homebrew not found. Please install Homebrew from https://brew.sh/")
-#                 sys.exit(1)
-#             run_command(["brew", "install", "r"])
-#     elif os_type == "Windows":
-#         if not is_command_available("Rscript"):
-#             print("R is not installed. Please install R from https://cran.r-project.org/bin/windows/base/")
-#             sys.exit(1)
-#     else:
-#         print(f"Unsupported OS: {os_type}. Please install R and its dependencies manually.")
-#         sys.exit(1)
-#     print("System dependencies for R installed successfully.\n")
-
-# def install_r_dependencies(os_type):
-#     print("Installing R dependencies...")
-#     r_packages = [
-#         'dplyr',
-#         'SmCCNet',
-#         'jsonlite'
-#     ]
-#     bioc_packages = [
-#         'impute',
-#         'preprocessCore',
-#         'GO.db',
-#         'AnnotationDbi'
-#     ]
-
-#     if r_packages:
-#         r_cran_packages = ', '.join([f'"{pkg}"' for pkg in r_packages])
-#         r_cran_install = f'install.packages(c({r_cran_packages}), repos="https://cran.r-project.org")'
-#         run_command(["Rscript", "-e", r_cran_install])
-
-#     if bioc_packages:
-#         run_command([
-#             "Rscript",
-#             "-e",
-#             "if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager', repos='https://cran.r-project.org')"
-#         ])
-#         bioc_install_packages = ', '.join([f'"{pkg}"' for pkg in bioc_packages])
-#         bioc_install = f'BiocManager::install(c({bioc_install_packages}), update=FALSE, ask=FALSE)'
-#         run_command(["Rscript", "-e", bioc_install])
-
-#     run_command([
-#         "Rscript",
-#         "-e",
-#         "install.packages('WGCNA', repos='https://cran.r-project.org')"
-#     ])
-
-#     print("R dependencies installed successfully.\n")
-
-# def create_virtual_env(venv_path):
-#     print(f"Creating virtual environment at {venv_path}...")
-#     run_command([sys.executable, "-m", "venv", venv_path])
-#     print("Virtual environment created successfully.\n")
-
-# def install_python_dependencies(requirements, venv_path, os_type):
-#     print("Installing Python dependencies...")
-#     if os_type == "Windows":
-#         pip_executable = os.path.join(venv_path, "Scripts", "pip.exe")
-#     else:
-#         pip_executable = os.path.join(venv_path, "bin", "pip")
-
-#     run_command([pip_executable, "install", "--upgrade", "pip", "setuptools", "wheel", "--no-cache-dir", "-q"])
-#     run_command([pip_executable, "install", "-r", requirements, "--no-cache-dir", "-q"])
-
-#     print("Python dependencies installed successfully.\n")
-
-# def get_installed_torch_version(venv_path, os_type):
-#     if os_type == "Windows":
-#         python_executable = os.path.join(venv_path, "Scripts", "python.exe")
-#     else:
-#         python_executable = os.path.join(venv_path, "bin", "python")
-
-#     try:
-#         version = subprocess.check_output(
-#             [python_executable, "-c", "import torch; print(torch.__version__)"],
-#             stderr=subprocess.STDOUT
-#         ).decode('utf-8').strip()
-#         return version
-#     except subprocess.CalledProcessError:
-#         print("Failed to retrieve PyTorch version.")
-#         return None
-
-# def get_pytorch_cuda_version(venv_path, os_type):
-#     if os_type == "Windows":
-#         python_executable = os.path.join(venv_path, "Scripts", "python.exe")
-#     else:
-#         python_executable = os.path.join(venv_path, "bin", "python")
-
-#     try:
-#         cuda_available = subprocess.check_output(
-#             [python_executable, "-c", "import torch; print(torch.version.cuda)"],
-#             stderr=subprocess.STDOUT
-#         ).decode('utf-8').strip()
-#         if cuda_available and cuda_available.lower() != "none":
-#             return cuda_available
-#         else:
-#             return "cpu"
-#     except subprocess.CalledProcessError:
-#         print("Failed to retrieve PyTorch CUDA version.")
-#         return "cpu"
-
-# def get_pyg_os_arch(os_type):
-#     if os_type == "Linux":
-#         return "linux_x86_64"
-#     elif os_type == "Windows":
-#         return "cp310-cp310-win_amd64"
-#     elif os_type == "Darwin":
-#         arch = platform.machine()
-#         if arch == "x86_64":
-#             return "macosx_10_15_x86_64"
-#         elif arch in ["arm64", "aarch64"]:
-#             return "macosx_10_15_arm64"
-#         else:
-#             print(f"Unsupported macOS architecture: {arch}")
-#             sys.exit(1)
-#     else:
-#         print(f"Unsupported OS: {os_type}. Cannot determine PyG wheel architecture.")
-#         sys.exit(1)
-
-# def install_torch(install_type, cuda_version, venv_path, os_type):
-#     print("Installing PyTorch...")
-#     if os_type == "Windows":
-#         pip_executable = os.path.join(venv_path, "Scripts", "pip.exe")
-#     else:
-#         pip_executable = os.path.join(venv_path, "bin", "pip")
-
-#     if install_type == "2":
-#         if not cuda_version:
-#             cuda_version = detect_cuda_version()
-#             if not cuda_version:
-#                 print("No CUDA installation detected. Proceeding with CPU-only PyTorch installation.")
-#                 install_type = "1"
-
-#         if install_type == "2" and cuda_version:
-#             supported_cuda_versions = ["11.8", "12.1", "12.4"]
-#             if cuda_version not in supported_cuda_versions:
-#                 print(f"Unsupported CUDA version detected: {cuda_version}. Supported versions are {supported_cuda_versions}.")
-#                 sys.exit(1)
-
-#             torch_index_url = f"https://download.pytorch.org/whl/cu{cuda_version.replace('.', '')}"
-#             print(f"Installing PyTorch with CUDA {cuda_version} support...")
-#             run_command([
-#                 pip_executable, "install",
-#                 "torch", "torchvision", "torchaudio",
-#                 "--index-url", torch_index_url,
-#                 "--no-cache-dir",
-#                 "-q"
-#             ])
-#     else:
-#         torch_index_url = "https://download.pytorch.org/whl/cpu"
-#         print("Installing PyTorch CPU-only...")
-#         run_command([
-#             pip_executable, "install",
-#             "torch", "torchvision", "torchaudio",
-#             "--index-url", torch_index_url,
-#             "--no-cache-dir",
-#             "-q"
-#         ])
-
-#     print("PyTorch installed successfully.\n")
-
-# def install_extra_packages(venv_path, os_type):
-#     print("Installing torch-scatter, torch-sparse, and torch-geometric...")
-#     if os_type == "Windows":
-#         pip_executable = os.path.join(venv_path, "Scripts", "pip.exe")
-#     else:
-#         pip_executable = os.path.join(venv_path, "bin", "pip")
-
-#     torch_version = get_installed_torch_version(venv_path, os_type)
-#     pytorch_cuda_version = get_pytorch_cuda_version(venv_path, os_type)
-
-#     if not torch_version:
-#         print("PyTorch is not installed correctly. Cannot proceed with installing related packages.")
-#         sys.exit(1)
-
-#     print(f"Installed PyTorch version: {torch_version}")
-#     print(f"Installed CUDA version: {pytorch_cuda_version}\n")
-
-#     if pytorch_cuda_version == "cpu":
-#         pyg_url = f"https://data.pyg.org/whl/torch-{torch_version}+cpu.html"
-#     else:
-#         cuda_no_dot = pytorch_cuda_version.replace('.', '')
-#         pyg_url = f"https://data.pyg.org/whl/torch-{torch_version}+cu{cuda_no_dot}.html"
-
-#     print(f"Using PyG wheel URL: {pyg_url}\n")
-
-#     run_command([
-#         pip_executable, "install",
-#         "torch-scatter", "torch-sparse",
-#         "-f", pyg_url,
-#         "--no-cache-dir",
-#         "-q"
-#     ])
-
-#     run_command([
-#         pip_executable, "install",
-#         "torch-geometric",
-#         "--no-cache-dir",
-#         "-q"
-#     ])
-
-#     print("torch-scatter, torch-sparse, and torch-geometric installed successfully.\n")
-
-# def install_dev_dependencies(dev_requirements, venv_path, os_type):
-#     print("Installing development dependencies...")
-#     install_python_dependencies(dev_requirements, venv_path, os_type)
-#     print("Development dependencies installed successfully.\n")
-
-# def main():
-#     args = parse_arguments()
-
-#     install_type = "1"
-#     if args.cuda:
-#         install_type = "2"
-
-#     cuda_version = None
-#     if args.cuda_version:
-#         cuda_version = args.cuda_version
-
-#     install_dev = args.dev
-
-#     required_python_version = (3, 10)
-#     current_python_version = sys.version_info
-#     if current_python_version < required_python_version:
-#         print(f"Python {required_python_version[0]}.{required_python_version[1]} or higher is required.")
-#         print(f"Current Python version: {current_python_version.major}.{current_python_version.minor}")
-#         sys.exit(1)
-#     else:
-#         print(f"Python version {current_python_version.major}.{current_python_version.minor} detected. Proceeding...\n")
-
-#     os_type = platform.system()
-#     print(f"Detected Operating System: {os_type}\n")
-
-#     current_dir = os.getcwd()
-#     scripts_dir = os.path.join(current_dir, "scripts")
-#     requirements_txt = os.path.join(current_dir, "requirements.txt")
-#     dev_requirements = os.path.join(scripts_dir, "requirements-dev.txt")
-#     venv_path = os.path.join(current_dir, "bioneuralnet-env")
-
-#     install_r_system_dependencies(os_type)
-
-#     if not os.path.exists(venv_path):
-#         create_virtual_env(venv_path)
-#     else:
-#         print(f"Virtual environment already exists at {venv_path}.\n")
-
-#     install_python_dependencies(requirements_txt, venv_path, os_type)
-
-#     if install_dev:
-#         install_dev_dependencies(dev_requirements, venv_path, os_type)
-#     else:
-#         print("Skipping development dependencies installation.\n")
-
-#     install_torch(install_type, cuda_version, venv_path, os_type)
-#     install_extra_packages(venv_path, os_type)
-#     install_r_dependencies(os_type)
-
-#     print("\n--------------------------")
-#     print("BioNeuralNet setup complete!")
-#     print("--------------------------\n")
-#     print("To activate the virtual environment, run:")
-#     if os_type == "Windows":
-#         print("bioneuralnet-env\\Scripts\\activate\n")
-#     else:
-#         print("source ./bioneuralnet-env/bin/activate\n")
-#     print("To deactivate the virtual environment, run:")
-#     print("deactivate\n")
-
-# if __name__ == "__main__":
-#     main()

From 7d42a0906c816befae9c946168f180b952a35cc0 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 23 Jan 2025 16:46:52 -0700
Subject: [PATCH 14/26] Cleaned up index and readme

---
 README.md             | 116 +++++++++++++++++++++++++++++++-----------
 docs/source/index.rst |   9 ++--
 2 files changed, 92 insertions(+), 33 deletions(-)

diff --git a/README.md b/README.md
index bfcff26..19f47e7 100644
--- a/README.md
+++ b/README.md
@@ -159,37 +159,46 @@ Below is a quick example demonstrating the following:
 ### Code Example:
 
 ```python
-import pandas as pd
 from bioneuralnet.external_tools import SmCCNet
 from bioneuralnet.downstream_task import DPMON
+import pandas as pd
 
-# Step 1: Data Preparation
-phenotype_data = pd.read_csv('phenotype_data.csv', index_col=0)
-omics_proteins = pd.read_csv('omics_proteins.csv', index_col=0)
-omics_metabolites = pd.read_csv('omics_metabolites.csv', index_col=0)
-clinical_data = pd.read_csv('clinical_data.csv', index_col=0)
+# 1) Prepare data
+omics_data = pd.read_csv("data/omics_data.csv")
+phenotype_data = pd.read_csv("data/phenotype_data.csv")
+clinical_data = pd.read_csv("data/clinical_data.csv")
 
-# Step 2: Network Construction
+# 2) Run SmCCNet to get adjacency
 smccnet = SmCCNet(
-    phenotype_df=phenotype_data,
-    omics_dfs=[omics_proteins, omics_metabolites],
-    data_types=["protein", "metabolite"],
-    kfold=5,
-    summarization="PCA",
-)
+   phenotype_df=phenotype_data,
+   omics_df=omics_data,
+   data_types=["genes, proteins"]
+   kfolds=5,
+   summarization = "NetSHy",
+   seed: 127,
+   )
 adjacency_matrix = smccnet.run()
-print("Adjacency matrix generated.")
 
-# Step 3: Disease Prediction
+# 3) Disease Prediction with DPMON
 dpmon = DPMON(
-    adjacency_matrix=adjacency_matrix,
-    omics_list=[omics_proteins, omics_metabolites],
-    phenotype_data=phenotype_data,
-    clinical_data=clinical_data,
-    model="GAT",
+   adjacency_matrix=adjacency_matrix,
+   omics_list=[omics_data],
+   phenotype_data=phenotype_data,
+   clinical_data=clinical_data,
+   model: "GAT",
+   gnn_hidden_dim: 64,
+   layer_num: 3,
+   nn_hidden_dim1: 2,
+   nn_hidden_dim2: 2,
+   epoch_num: 10,
+   repeat_num: 5,
+   lr: 0.01,
+   weight_decay: 1e-4,
+   tune: True,
+   gpu: False
 )
 predictions = dpmon.run()
-print("Disease phenotype predictions:\n", predictions)
+print("Disease predictions:\n", predictions)
 ```
 
 ### Output
@@ -221,14 +230,57 @@ See [FAQ](https://bioneuralnet.readthedocs.io/en/latest/faq.html) for more.
 
 ## Acknowledgments
 
-BioNeuralNet relies on or interfaces with various open-source libraries:
+BioNeuralNet relies on and interfaces with various open-source libraries. We extend our gratitude to the developers and contributors of these projects for their invaluable tools and resources.
+
+### Core Dependencies
+
+- [PyYAML](https://pyyaml.org/) - **MIT License**
+- [pandas](https://pandas.pydata.org/) - **BSD 3-Clause License**
+- [numpy](https://numpy.org/) - **BSD 3-Clause License**
+- [scikit-learn](https://scikit-learn.org/) - **BSD 3-Clause License**
+- [node2vec](https://github.com/aditya-grover/node2vec) - **MIT License**
+- [matplotlib](https://matplotlib.org/) - **Matplotlib License**
+- [ray](https://github.com/ray-project/ray) - **Apache 2.0 License**
+- [tensorboardX](https://github.com/lanpa/tensorboardX) - **MIT License**
+- [networkx](https://networkx.org/) - **BSD License**
+- [pyvis](https://github.com/WestHealth/pyvis) - **MIT License**
+- [leidenalg](https://github.com/vtraag/leidenalg) - **GNU LGPL v3**
+- [dtt](https://github.com/BioroboticsLab/dtt) - **MIT License**
+- [pyreadr](https://github.com/ofajardo/pyreadr) - **MIT License**
+
+### Development Dependencies
+
+These tools are essential for the development and maintenance of BioNeuralNet but are not required for end-users.
+
+- [pytest](https://pytest.org/) - **MIT License**
+- [pytest-cov](https://pytest-cov.readthedocs.io/) - **MIT License**
+- [pytest-mock](https://github.com/pytest-dev/pytest-mock) - **MIT License**
+- [Sphinx](https://www.sphinx-doc.org/) - **BSD License**
+- [Sphinx RTD Theme](https://sphinx-rtd-theme.readthedocs.io/) - **BSD License**
+- [sphinx-autosummary-accessors](https://github.com/kennethreitz/sphinx-autosummary-accessors) - **MIT License**
+- [sphinxcontrib-napoleon](https://sphinxcontrib-napoleon.readthedocs.io/) - **BSD License**
+- [flake8](https://flake8.pycqa.org/) - **MIT License**
+- [Black](https://black.readthedocs.io/) - **MIT License**
+- [mypy](http://mypy-lang.org/) - **MIT License**
+- [pre-commit](https://pre-commit.com/) - **MIT License**
+- [tox](https://tox.readthedocs.io/) - **MIT License**
+- [setuptools](https://setuptools.pypa.io/) - **MIT License**
+- [twine](https://twine.readthedocs.io/) - **MIT License**
+- [torch](https://pytorch.org/) - **BSD License**
+- [torch_geometric](https://github.com/pyg-team/pytorch_geometric) - **MIT License**
+
+### External Tools
+
+BioNeuralNet integrates with external tools to enhance functionality:
 
-- [PyTorch](https://pytorch.org/) / [PyTorch Geometric](https://github.com/pyg-team/pytorch_geometric)
-- [Node2Vec](https://github.com/aditya-grover/node2vec)
-- [WGCNA](https://cran.r-project.org/package=WGCNA) / [SmCCNet](https://cran.r-project.org/package=SmCCNet)
-- [Pytest](https://pytest.org/), [Sphinx](https://www.sphinx-doc.org), [Black](https://black.readthedocs.io/), [Flake8](https://flake8.pycqa.org/)
+- [WGCNA](https://cran.r-project.org/package=WGCNA) - **GPL-3.0 License**
+- [SmCCNet](https://cran.r-project.org/package=SmCCNet) - **GPL-3.0 License**
 
-We appreciate the efforts of these communities and all contributors.
+### Special Thanks
+
+We appreciate the efforts of these communities and all contributors who make open-source development possible. Your dedication and hard work enable projects like BioNeuralNet to thrive and evolve.
+
+---
 
 ## Testing & CI
 
@@ -250,10 +302,14 @@ We appreciate the efforts of these communities and all contributors.
 For more details, see our [FAQ](https://bioneuralnet.readthedocs.io/en/latest/faq.html)
 or open an [issue](https://github.com/UCD-BDLab/BioNeuralNet/issues).
 
-## License & Contact
+## License
+
+- **License:** [MIT License](https://github.com/UCD-BDLab/BioNeuralNet/blob/main/LICENSE)
+
+## Contact
 
-- **License**: [MIT License](https://github.com/UCD-BDLab/BioNeuralNet/blob/main/LICENSE)
-- **Contact**: Questions or feature requests? [Open an issue](https://github.com/UCD-BDLab/BioNeuralNet) or email [vicente.ramos@ucdenver.edu](mailto:vicente.ramos@ucdenver.edu).
+- **Questions or Feature Requests:** [Open an issue](https://github.com/UCD-BDLab/BioNeuralNet/issues)
+- **Email:** [vicente.ramos@ucdenver.edu](mailto:vicente.ramos@ucdenver.edu)
 
 ---
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 069be73..6ebddb5 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -11,11 +11,14 @@ Welcome to BioNeuralNet Beta 0.1
    :target: https://pypi.org/project/bioneuralnet/
 .. image:: https://static.pepy.tech/badge/bioneuralnet
    :target: https://pepy.tech/project/bioneuralnet
+.. image:: https://img.shields.io/badge/GitHub-View%20Code-blue
+   :target: https://github.com/UCD-BDLab/BioNeuralNet
 
 .. figure:: _static/LOGO_WB.png
    :align: center
    :alt: bioneuralnet logo
 
+
 **Note:** This is a **beta version** of BioNeuralNet. It is under active development, and certain features
 may be incomplete or subject to change. Feedback and bug reports are highly encouraged to help us
 improve the tool.
@@ -118,7 +121,9 @@ Below is a quick example demonstrating the following steps:
   - **Adjacency Matrix**: The multi-omics network representation.
   - **Predictions**: Disease phenotype predictions for each sample.
 
-**Note**: For a complete script, see `examples/example_2.py` in the repository.
+.. note::
+
+   - For a complete script of DPMON and subject representation example, see :doc:`tutorials`.
 
 **BioNeuralNet Overview: Multi-Omics Integration with Graph Neural Networks**
 -----------------------------------------------------------------------------
@@ -156,8 +161,6 @@ BioNeuralNet offers five core steps in a typical workflow:
 
    BioNeuralNet Overview
 
-Documentation Overview
-----------------------
 
 .. toctree::
    :maxdepth: 2

From 592f117f7c04aa1878318b5021780a01bc2c3bf6 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Sun, 26 Jan 2025 22:16:16 -0700
Subject: [PATCH 15/26] Added sample dataset and GNN models refactoring After
 review and merge I will release a new version of the package: beta version
 0.2.0b1 Need to add testing for metrics code.

---
 .gitignore                                    |  10 +-
 .pre-commit-config.yaml                       |  20 +-
 MANIFEST.in                                   |   3 -
 README.md                                     |   6 +-
 bioneuralnet/__init__.py                      |  20 +-
 bioneuralnet/analysis/__init__.py             |   3 -
 bioneuralnet/analysis/correlation_analysis.py |  15 -
 bioneuralnet/clustering/__init__.py           |   5 +-
 bioneuralnet/clustering/correlated_louvain.py |  20 +
 .../{pagerank.py => correlated_pagerank.py}   |   1 -
 bioneuralnet/datasets/__init__.py             |   3 +
 bioneuralnet/datasets/dataset_loader.py       |  51 +++
 bioneuralnet/datasets/example1/X1.csv         | 359 ++++++++++++++++++
 bioneuralnet/datasets/example1/X2.csv         | 359 ++++++++++++++++++
 bioneuralnet/datasets/example1/Y.csv          | 359 ++++++++++++++++++
 bioneuralnet/downstream_task/dpmon.py         | 113 +-----
 bioneuralnet/external_tools/SmCCNet.R         | 123 +++---
 bioneuralnet/external_tools/smccnet.py        |  85 ++---
 bioneuralnet/metrics/__init__.py              |   7 +
 bioneuralnet/metrics/correlation.py           |  32 ++
 bioneuralnet/metrics/f1_score.py              |  56 +++
 bioneuralnet/metrics/mse.py                   |  32 ++
 bioneuralnet/metrics/precision.py             |  46 +++
 bioneuralnet/metrics/recall.py                |  46 +++
 .../network_embedding/gnn_embedding.py        | 291 ++++++++------
 bioneuralnet/network_embedding/gnn_models.py  | 146 +++----
 .../subject_representation.py                 | 224 ++++++-----
 docs/source/clustering.rst                    |   6 +
 docs/source/examples/example_1.py             |   9 +-
 docs/source/gnns.rst                          | 110 ++++--
 docs/source/index.rst                         |   1 +
 docs/source/tutorials/example_1.rst           |  13 +-
 tests/__init__.py                             |   1 +
 tests/test_dataset_dpmon.py                   |  63 +++
 tests/test_dataset_gnn_embeddings.py          |  71 ++++
 tests/test_dataset_subject_representation.py  |  59 +++
 tests/test_dpmon.py                           |  22 +-
 tests/test_gnn_embedding.py                   | 307 ++++++++++++++-
 tests/test_pagerank.py                        | 260 ++++++-------
 tests/test_smccnet.py                         | 128 ++++---
 tests/test_subject_representation.py          | 164 ++++++--
 41 files changed, 2780 insertions(+), 869 deletions(-)
 delete mode 100644 bioneuralnet/analysis/__init__.py
 delete mode 100644 bioneuralnet/analysis/correlation_analysis.py
 create mode 100644 bioneuralnet/clustering/correlated_louvain.py
 rename bioneuralnet/clustering/{pagerank.py => correlated_pagerank.py} (99%)
 create mode 100644 bioneuralnet/datasets/__init__.py
 create mode 100644 bioneuralnet/datasets/dataset_loader.py
 create mode 100644 bioneuralnet/datasets/example1/X1.csv
 create mode 100644 bioneuralnet/datasets/example1/X2.csv
 create mode 100644 bioneuralnet/datasets/example1/Y.csv
 create mode 100644 bioneuralnet/metrics/__init__.py
 create mode 100644 bioneuralnet/metrics/correlation.py
 create mode 100644 bioneuralnet/metrics/f1_score.py
 create mode 100644 bioneuralnet/metrics/mse.py
 create mode 100644 bioneuralnet/metrics/precision.py
 create mode 100644 bioneuralnet/metrics/recall.py
 create mode 100644 docs/source/clustering.rst
 create mode 100644 tests/test_dataset_dpmon.py
 create mode 100644 tests/test_dataset_gnn_embeddings.py
 create mode 100644 tests/test_dataset_subject_representation.py

diff --git a/.gitignore b/.gitignore
index b41f980..ff3314a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,10 +16,18 @@ bioneuralnet.egg-info
 /docker_files/
 .mypy_cache
 
-# Block sensitive file types globally
+# Blocking sensitive file types globally
 *.csv
 *.RData
+
+# Allowing .csv files in datasets/example1 and its subdirectories
+!bioneuralnet/datasets/
+!bioneuralnet/datasets/example1/
+!bioneuralnet/datasets/example1/**/*.csv
+
 dpmon_output/
+todo.md
+ignore_dir/
 
 
 # Sphinx documentation build
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e0861d4..a7aebec 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -8,6 +8,8 @@ repos:
         exclude: '\.svg$|docs/source/_autosummary/'
       - id: check-yaml
       - id: check-added-large-files
+        args: ['--maxkb=500']
+        exclude: '^bioneuralnet/datasets/example1/'
 
   - repo: https://github.com/psf/black
     rev: 24.10.0
@@ -38,10 +40,22 @@ repos:
         stages: [pre-commit]
 
       - id: check-forbidden-files
-        name: Prevent adding forbidden file types
-        entry: bash -c "if git diff --cached --name-only | grep -E '\\.csv$|\\.RData$'; then echo 'Forbidden file types detected!'; exit 1; fi"
+        name: Prevent adding forbidden file types except in allowed directory
+        entry: |
+          bash -c '
+          FILES=$(git diff --cached --name-only)
+          if echo "$FILES" | grep -E "\.(RData)$"; then
+            echo "Forbidden file types detected (RData)!"
+            exit 1
+          fi
+          if echo "$FILES" | grep -E "\.csv$" | grep -v "^bioneuralnet/datasets/example1/"; then
+            echo "Forbidden file types detected (CSV outside example1)!"
+            exit 1
+          fi
+          '
         language: system
-        types: [file]
+        stages: [pre-commit]
+
 
       - id: run-tests
         name: Run Tests with Pytest
diff --git a/MANIFEST.in b/MANIFEST.in
index c9df115..611c6fc 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -11,9 +11,6 @@ recursive-include docs *
 # Include example scripts
 recursive-include examples *.py
 
-# Include scripts installation files
-recursive-include scripts *
-
 # Exclude __pycache__ and compiled Python files
 global-exclude __pycache__ *.pyc *.pyo *.pyd
 
diff --git a/README.md b/README.md
index 19f47e7..92b70ab 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,8 @@
 ![GitHub Contributors](https://img.shields.io/github/contributors/UCD-BDLab/BioNeuralNet)
 ![Downloads](https://static.pepy.tech/badge/bioneuralnet)
 
+[![Documentation](https://img.shields.io/badge/docs-read%20the%20docs-blue.svg)](https://bioneuralnet.readthedocs.io/en/latest/)
+
 
 ## Welcome to [BioNeuralNet Beta 0.1](https://bioneuralnet.readthedocs.io/en/latest/index.html)
 
@@ -247,6 +249,8 @@ BioNeuralNet relies on and interfaces with various open-source libraries. We ext
 - [leidenalg](https://github.com/vtraag/leidenalg) - **GNU LGPL v3**
 - [dtt](https://github.com/BioroboticsLab/dtt) - **MIT License**
 - [pyreadr](https://github.com/ofajardo/pyreadr) - **MIT License**
+- [torch](https://pytorch.org/) - **BSD License**
+- [torch_geometric](https://github.com/pyg-team/pytorch_geometric) - **MIT License**
 
 ### Development Dependencies
 
@@ -266,8 +270,6 @@ These tools are essential for the development and maintenance of BioNeuralNet bu
 - [tox](https://tox.readthedocs.io/) - **MIT License**
 - [setuptools](https://setuptools.pypa.io/) - **MIT License**
 - [twine](https://twine.readthedocs.io/) - **MIT License**
-- [torch](https://pytorch.org/) - **BSD License**
-- [torch_geometric](https://github.com/pyg-team/pytorch_geometric) - **MIT License**
 
 ### External Tools
 
diff --git a/bioneuralnet/__init__.py b/bioneuralnet/__init__.py
index 6c32e4c..b0fed8c 100644
--- a/bioneuralnet/__init__.py
+++ b/bioneuralnet/__init__.py
@@ -26,6 +26,13 @@
 from .network_embedding import GNNEmbedding
 from .subject_representation import GraphEmbedding
 from .downstream_task import DPMON
+from .clustering import PageRank
+from .clustering import Louvain
+from .metrics import correlation
+from .metrics import mse
+from .metrics import f1_score
+from .metrics import precision
+from .metrics import recall
 
 from .external_tools import DynamicVisualizer
 from .external_tools import FeatureSelector
@@ -36,14 +43,19 @@
 from .external_tools import Node2Vec
 
 __all__: list = [
-    "network_embedding",
-    "subject_representation",
-    "utils",
     "__version__",
     "GNNEmbedding",
-    "Node2Vec",
     "GraphEmbedding",
     "DPMON",
+    "PageRank",
+    "Louvain",
+    "correlation",
+    "mse",
+    "f1_score",
+    "precision",
+    "recall",
+    "utils",
+    "Node2Vec",
     "FeatureSelector",
     "StaticVisualizer",
     "DynamicVisualizer",
diff --git a/bioneuralnet/analysis/__init__.py b/bioneuralnet/analysis/__init__.py
deleted file mode 100644
index b1eb47c..0000000
--- a/bioneuralnet/analysis/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .correlation_analysis import Correlation
-
-__all__ = ["Correlation"]
diff --git a/bioneuralnet/analysis/correlation_analysis.py b/bioneuralnet/analysis/correlation_analysis.py
deleted file mode 100644
index 21e0657..0000000
--- a/bioneuralnet/analysis/correlation_analysis.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import pandas as pd
-
-
-class Correlation:
-    def __init__(self, data: pd.DataFrame):
-        self.data = data
-
-    def calculate_correlation(self) -> pd.DataFrame:
-        """
-        Calculates the correlation matrix for the input data.
-
-        Returns:
-            pd.DataFrame: The correlation matrix.
-        """
-        return self.data.corr()
diff --git a/bioneuralnet/clustering/__init__.py b/bioneuralnet/clustering/__init__.py
index 0498a96..15ac6af 100644
--- a/bioneuralnet/clustering/__init__.py
+++ b/bioneuralnet/clustering/__init__.py
@@ -1,3 +1,4 @@
-from .pagerank import PageRank
+from .correlated_pagerank import PageRank
+from .correlated_louvain import Louvain
 
-__all__ = ["PageRank"]
+__all__ = ["PageRank", "Louvain"]
diff --git a/bioneuralnet/clustering/correlated_louvain.py b/bioneuralnet/clustering/correlated_louvain.py
new file mode 100644
index 0000000..0da242f
--- /dev/null
+++ b/bioneuralnet/clustering/correlated_louvain.py
@@ -0,0 +1,20 @@
+import pandas as pd
+
+
+class Louvain:
+    """
+    Correlated Louvain:
+
+    Attributes:
+    """
+
+    def __init__(self, data: pd.DataFrame):
+        self.data = data
+
+    def run(self) -> pd.DataFrame:
+        """
+
+        Returns:
+            pd.DataFrame:
+        """
+        return self.data
diff --git a/bioneuralnet/clustering/pagerank.py b/bioneuralnet/clustering/correlated_pagerank.py
similarity index 99%
rename from bioneuralnet/clustering/pagerank.py
rename to bioneuralnet/clustering/correlated_pagerank.py
index 3068076..0c32dda 100644
--- a/bioneuralnet/clustering/pagerank.py
+++ b/bioneuralnet/clustering/correlated_pagerank.py
@@ -22,7 +22,6 @@ class PageRank:
         tol (float): Tolerance for convergence.
         k (float): Weighting factor for composite correlation-conductance score.
         output_dir (str): Directory to save outputs.
-        logger (logging.Logger): Logger for the class.
     """
 
     def __init__(
diff --git a/bioneuralnet/datasets/__init__.py b/bioneuralnet/datasets/__init__.py
new file mode 100644
index 0000000..4932dbc
--- /dev/null
+++ b/bioneuralnet/datasets/__init__.py
@@ -0,0 +1,3 @@
+from .dataset_loader import DatasetLoader
+
+__all__ = ["DatasetLoader"]
diff --git a/bioneuralnet/datasets/dataset_loader.py b/bioneuralnet/datasets/dataset_loader.py
new file mode 100644
index 0000000..616da1c
--- /dev/null
+++ b/bioneuralnet/datasets/dataset_loader.py
@@ -0,0 +1,51 @@
+import os
+import pandas as pd
+from typing import Tuple, Optional
+
+
+class DatasetLoader:
+    """
+    This class allows users to load predefined sample datasets by specifying
+    the dataset name. Each dataset should contain three CSV files:
+    - omics1.csv: Genomic features
+    - omics2.csv: miRNA features
+    - pheno.csv: Phenotype data with a single 'Pheno' column representing the target outcome
+    """
+
+    def __init__(self, base_dir: Optional[str] = None):
+        """
+        Initializes the DatasetLoader with the base data directory.
+        """
+        if base_dir is None:
+            self.base_dir = os.path.join(os.path.dirname(__file__), "example1")
+        else:
+            self.base_dir = base_dir
+
+    def __call__(self, name: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        """
+        Loads the specified dataset.
+        Args: name (str): Name of the dataset to load (corresponds to a subdirectory in the datasets folder).
+        Returns: tuple: A tuple containing the omics1, omics2, and pheno DataFrames.
+        Raises: FileNotFoundError if the required files are not found.
+        """
+        dataset_path = os.path.join(os.path.dirname(__file__), name)
+        if not os.path.isdir(dataset_path):
+            raise ValueError(
+                f"Dataset '{name}' not found in '{os.path.dirname(__file__)}'."
+            )
+
+        omics1_path = os.path.join(dataset_path, "X1.csv")
+        omics2_path = os.path.join(dataset_path, "X2.csv")
+        pheno_path = os.path.join(dataset_path, "Y.csv")
+
+        for path in [omics1_path, omics2_path, pheno_path]:
+            if not os.path.isfile(path):
+                raise FileNotFoundError(
+                    f"Required file '{os.path.basename(path)}' not found in '{dataset_path}'."
+                )
+
+        omics1 = pd.read_csv(omics1_path, index_col=0)
+        omics2 = pd.read_csv(omics2_path, index_col=0)
+        pheno = pd.read_csv(pheno_path, index_col=0)
+
+        return omics1, omics2, pheno
diff --git a/bioneuralnet/datasets/example1/X1.csv b/bioneuralnet/datasets/example1/X1.csv
new file mode 100644
index 0000000..6ca65c2
--- /dev/null
+++ b/bioneuralnet/datasets/example1/X1.csv
@@ -0,0 +1,359 @@
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diff --git a/bioneuralnet/datasets/example1/X2.csv b/bioneuralnet/datasets/example1/X2.csv
new file mode 100644
index 0000000..f80165d
--- /dev/null
+++ b/bioneuralnet/datasets/example1/X2.csv
@@ -0,0 +1,359 @@
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diff --git a/bioneuralnet/datasets/example1/Y.csv b/bioneuralnet/datasets/example1/Y.csv
new file mode 100644
index 0000000..f96ba98
--- /dev/null
+++ b/bioneuralnet/datasets/example1/Y.csv
@@ -0,0 +1,359 @@
+"","Pheno"
+"Samp_1",235.06742273556
+"Samp_2",253.544991042769
+"Samp_3",234.204994225565
+"Samp_4",281.035428853511
+"Samp_5",245.447780675048
+"Samp_6",189.623099755617
+"Samp_7",234.250684995099
+"Samp_8",250.346772856151
+"Samp_9",248.257251273706
+"Samp_10",260.19391506098
+"Samp_11",262.065504162804
+"Samp_12",243.846251348497
+"Samp_13",225.544786850157
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diff --git a/bioneuralnet/downstream_task/dpmon.py b/bioneuralnet/downstream_task/dpmon.py
index d375782..f205492 100644
--- a/bioneuralnet/downstream_task/dpmon.py
+++ b/bioneuralnet/downstream_task/dpmon.py
@@ -20,7 +20,7 @@
 from ray import train
 from ray.train import Checkpoint
 
-from torch_geometric.nn import GCNConv, GATConv, SAGEConv, GINConv
+from ..network_embedding.gnn_models import GCN, GAT, SAGE, GIN
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
@@ -32,7 +32,7 @@ def __init__(
         adjacency_matrix: pd.DataFrame,
         omics_list: List[pd.DataFrame],
         phenotype_data: pd.DataFrame,
-        clinical_data: pd.DataFrame,
+        clinical_data: Optional[pd.DataFrame] = None,
         model: str = "GAT",
         gnn_hidden_dim: int = 16,
         layer_num: int = 5,
@@ -47,25 +47,18 @@ def __init__(
         cuda: int = 0,
         output_dir: Optional[str] = None,
     ):
-        if clinical_data is None or clinical_data.empty:
-            raise ValueError(
-                "Clinical data (features_file) is required and cannot be empty."
-            )
-
-        if (
-            phenotype_data is None
-            or phenotype_data.empty
-            or "phenotype" not in phenotype_data.columns
-        ):
-            raise ValueError(
-                "Phenotype data must contain 'phenotype' and cannot be empty."
-            )
 
+        if adjacency_matrix.empty:
+            raise ValueError("Adjacency matrix cannot be empty.")
         if not omics_list or any(df.empty for df in omics_list):
             raise ValueError("All provided omics data files must be non-empty.")
-
-        if adjacency_matrix is None or adjacency_matrix.empty:
-            raise ValueError("Adjacency matrix is required and cannot be empty.")
+        if phenotype_data.empty or "phenotype" not in phenotype_data.columns:
+            raise ValueError(f"Phenotype data must have column a phenotype column.")
+        if clinical_data is not None and clinical_data.empty:
+            logger.warning(
+                "Clinical data provided is empty => treating as None => random features."
+            )
+            clinical_data = None
 
         self.adjacency_matrix = adjacency_matrix
         self.omics_list = omics_list
@@ -500,86 +493,6 @@ def train_model(model, criterion, optimizer, train_data, train_labels, epoch_num
     return accuracy
 
 
-class GCN(nn.Module):
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True):
-        super(GCN, self).__init__()
-        self.convs = nn.ModuleList()
-        self.convs.append(GCNConv(input_dim, hidden_dim))
-        for _ in range(layer_num - 1):
-            self.convs.append(GCNConv(hidden_dim, hidden_dim))
-        self.dropout = dropout
-
-    def forward(self, data):
-        x, edge_index = data.x, data.edge_index
-        for conv in self.convs:
-            x = conv(x, edge_index)
-            x = F.relu(x)
-            if self.dropout:
-                x = F.dropout(x, training=self.training)
-        return x
-
-
-class GAT(nn.Module):
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True, heads=1):
-        super(GAT, self).__init__()
-        self.convs = nn.ModuleList()
-        self.convs.append(GATConv(input_dim, hidden_dim, heads=heads))
-        for _ in range(layer_num - 1):
-            self.convs.append(GATConv(hidden_dim * heads, hidden_dim, heads=heads))
-        self.dropout = dropout
-
-    def forward(self, data):
-        x, edge_index = data.x, data.edge_index
-        for conv in self.convs:
-            x = conv(x, edge_index)
-            x = F.elu(x)
-            if self.dropout:
-                x = F.dropout(x, training=self.training)
-        return x
-
-
-class SAGE(nn.Module):
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True):
-        super(SAGE, self).__init__()
-        self.convs = nn.ModuleList()
-        self.convs.append(SAGEConv(input_dim, hidden_dim))
-        for _ in range(layer_num - 1):
-            self.convs.append(SAGEConv(hidden_dim, hidden_dim))
-        self.dropout = dropout
-
-    def forward(self, data):
-        x, edge_index = data.x, data.edge_index
-        for conv in self.convs:
-            x = conv(x, edge_index)
-            x = F.relu(x)
-            if self.dropout:
-                x = F.dropout(x, training=self.training)
-        return x
-
-
-class GIN(nn.Module):
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True):
-        super(GIN, self).__init__()
-        self.convs = nn.ModuleList()
-        for i in range(layer_num):
-            nn_module = nn.Sequential(
-                nn.Linear(hidden_dim if i > 0 else input_dim, hidden_dim),
-                nn.ReLU(),
-                nn.Linear(hidden_dim, hidden_dim),
-            )
-            self.convs.append(GINConv(nn_module))
-        self.dropout = dropout
-
-    def forward(self, data):
-        x, edge_index = data.x, data.edge_index
-        for conv in self.convs:
-            x = conv(x, edge_index)
-            x = F.relu(x)
-            if self.dropout:
-                x = F.dropout(x, training=self.training)
-        return x
-
-
 class NeuralNetwork(nn.Module):
     def __init__(
         self,
@@ -600,12 +513,14 @@ def __init__(
                 input_dim=gnn_input_dim,
                 hidden_dim=gnn_hidden_dim,
                 layer_num=gnn_layer_num,
+                final_layer="none",
             )
         elif model_type == "GAT":
             self.gnn = GAT(
                 input_dim=gnn_input_dim,
                 hidden_dim=gnn_hidden_dim,
                 layer_num=gnn_layer_num,
+                final_layer="none",
             )
         elif model_type == "SAGE":
             self.gnn = SAGE(
@@ -613,6 +528,7 @@ def __init__(
                 hidden_dim=gnn_hidden_dim,
                 output_dim=gnn_hidden_dim,
                 layer_num=gnn_layer_num,
+                final_layer="none",
             )
         elif model_type == "GIN":
             self.gnn = GIN(
@@ -620,6 +536,7 @@ def __init__(
                 hidden_dim=gnn_hidden_dim,
                 output_dim=gnn_hidden_dim,
                 layer_num=gnn_layer_num,
+                final_layer="none",
             )
         else:
             raise ValueError(f"Unsupported GNN model type: {model_type}")
diff --git a/bioneuralnet/external_tools/SmCCNet.R b/bioneuralnet/external_tools/SmCCNet.R
index 2dc73f6..d0d9b39 100644
--- a/bioneuralnet/external_tools/SmCCNet.R
+++ b/bioneuralnet/external_tools/SmCCNet.R
@@ -8,12 +8,11 @@ library("dplyr")
 options(stringsAsFactors = FALSE)
 allowWGCNAThreads()
 
-json_input <- readLines(con = "stdin")
 
+json_input <- readLines(con = "stdin")
 if (length(json_input) == 0) {
   stop("No input data received.")
 }
-
 input_data <- fromJSON(paste(json_input, collapse = "\n"))
 
 if (!("phenotype" %in% names(input_data))) {
@@ -21,24 +20,26 @@ if (!("phenotype" %in% names(input_data))) {
 }
 
 phenotype_df <- read.csv(text = input_data$phenotype, stringsAsFactors = FALSE)
+if (!("SampleID" %in% colnames(phenotype_df))) {
+  stop("SampleID column not found in phenotype data.")
+}
+rownames(phenotype_df) <- phenotype_df$SampleID
 
-omics_list <- list()
 omics_keys <- grep("^omics_", names(input_data), value = TRUE)
 if (length(omics_keys) == 0) {
   stop("No omics data found in input.")
 }
-for (omics_key in omics_keys) {
-  omics_df <- read.csv(text = input_data[[omics_key]], stringsAsFactors = FALSE)
-  omics_values <- omics_df[, -1]
-  rownames(omics_values) <- omics_df[[1]]
-  omics_list[[omics_key]] <- as.matrix(omics_values)
+omics_df <- read.csv(text = input_data[[omics_keys[1]]], stringsAsFactors = FALSE)
+if (!("SampleID" %in% colnames(omics_df))) {
+  stop("SampleID column not found in omics data.")
 }
+rownames(omics_df) <- omics_df$SampleID
+omics_values <- as.matrix(omics_df[, -1])
 
 args <- commandArgs(trailingOnly = TRUE)
 if (length(args) != 4) {
   stop("Exactly 4 arguments must be supplied: data_types, kfold, summarization, seed")
 }
-
 data_types <- strsplit(args[1], ",")[[1]]
 kfold <- as.numeric(args[2])
 summarization <- args[3]
@@ -47,69 +48,75 @@ seed <- as.numeric(args[4])
 if (is.na(kfold) || is.na(seed)) {
   stop("kfold and seed must be numeric values.")
 }
-
 if (!(summarization %in% c("PCA", "SVD", "NetSHy"))) {
   stop("Summarization method must be 'PCA', 'SVD', or 'NetSHy'.")
 }
-
 set.seed(seed)
 
-omics_values <- omics_list[[1]]
-common_samples <- intersect(rownames(omics_values), phenotype_df[[1]])
+
+common_samples <- intersect(rownames(omics_values), rownames(phenotype_df))
 if (length(common_samples) == 0) {
   stop("No matching sample IDs between omics data and phenotype.")
 }
 omics_values <- omics_values[common_samples, , drop = FALSE]
-phenotype_df <- phenotype_df[match(common_samples, phenotype_df[[1]]), , drop = FALSE]
-
-message("dim(omics_values) = ", paste(dim(omics_values), collapse = " x "))
-message("dim(phenotype_df) = ", paste(dim(phenotype_df), collapse = " x "))
+phenotype_df <- phenotype_df[common_samples, , drop = FALSE]
 
 Y <- as.numeric(phenotype_df[[2]])
+if (any(is.na(Y))) {
+  stop("Phenotype vector contains NA values.")
+}
 
-if (length(data_types) == 1) {
-  message("Single-omics mode detected.")
-  if (summarization != "NetSHy") {
-    message("Overriding summarization method to 'NetSHy' for single-omics analysis.")
-    summarization <- "NetSHy"
+tryCatch(
+  {
+    if (length(data_types) == 1) {
+      message("Single-omics mode detected.")
+      if (summarization != "NetSHy") {
+        message("Overriding summarization method to 'NetSHy' for single-omics analysis.")
+        summarization <- "NetSHy"
+      }
+      if (length(unique(Y)) == 2) {
+        message("Binary phenotype detected. Setting ncomp_pls = 5 for single-omics PLS.")
+        ncomp_pls <- 5
+      } else {
+        ncomp_pls <- NULL
+      }
+    } else {
+      message("Multi-omics mode detected.")
+      ncomp_pls <- NULL
+    }
+
+    if (!is.null(ncomp_pls)) {
+      result <- fastAutoSmCCNet(
+        X = list(omics_values),
+        Y = Y,
+        DataType = data_types,
+        Kfold = kfold,
+        subSampNum = 50,
+        EvalMethod = "accuracy",
+        summarization = summarization,
+        ncomp_pls = ncomp_pls,
+        seed = seed
+      )
+    } else {
+      result <- fastAutoSmCCNet(
+        X = list(omics_values),
+        Y = Y,
+        DataType = data_types,
+        Kfold = kfold,
+        subSampNum = 50,
+        EvalMethod = "accuracy",
+        summarization = summarization,
+        seed = seed
+      )
+    }
+  },
+  error = function(e) {
+    message("Error during fastAutoSmCCNet execution: ", conditionMessage(e))
+    quit(status = 1)
   }
-  if (length(unique(Y)) == 2) {
-    message("Binary phenotype detected. Setting ncomp_pls to 5 for single-omics PLS.")
-    ncomp_pls <- 5
-  } else {
-    ncomp_pls <- NULL
-  }
-} else {
-  message("Multi-omics mode detected.")
-  ncomp_pls <- NULL
-}
+)
 
-if (!is.null(ncomp_pls)) {
-  result <- fastAutoSmCCNet(
-    X = list(omics_values),
-    Y = Y,
-    DataType = data_types,
-    Kfold = kfold,
-    subSampNum = 50,
-    EvalMethod = "accuracy",
-    summarization = summarization,
-    ncomp_pls = ncomp_pls,
-    seed = seed,
-  )
-} else {
-  result <- fastAutoSmCCNet(
-    X = list(omics_values),
-    Y = Y,
-    DataType = data_types,
-    Kfold = kfold,
-    subSampNum = 50,
-    EvalMethod = "accuracy",
-    summarization = summarization,
-    seed = seed,
-  )
-}
+write.csv(result$AdjacencyMatrix, file = "AdjacencyMatrix.csv", row.names = TRUE)
 
-result_json <- toJSON(result, pretty = TRUE, auto_unbox = TRUE)
-cat(result_json)
 
 quit(status = 0)
diff --git a/bioneuralnet/external_tools/smccnet.py b/bioneuralnet/external_tools/smccnet.py
index 0186337..ad154b8 100644
--- a/bioneuralnet/external_tools/smccnet.py
+++ b/bioneuralnet/external_tools/smccnet.py
@@ -58,9 +58,9 @@ def __init__(
     def preprocess_data(self) -> Dict[str, Any]:
         """
         Preprocesses (lightly validates) the phenotype and omics data:
-        - Checks that each omics DataFrame has the same number of rows as the phenotype DataFrame.
-        - Checks that the first column (sample IDs) match between phenotype and each omics DataFrame.
-            If they do not match, logs a warning and replaces them with sequential integer IDs.
+        - Includes sample IDs as a separate column.
+        - Checks that each omics DataFrame has the same number of samples as the phenotype DataFrame.
+        - Finds and retains common sample IDs between phenotype and each omics DataFrame.
         - Serializes the phenotype and each omics DataFrame to CSV.
 
         Returns:
@@ -70,45 +70,45 @@ def preprocess_data(self) -> Dict[str, Any]:
                             "omics_1", "omics_2", ...: CSV strings of each omics DataFrame.
         """
         self.logger.info("Validating phenotype and omics data...")
-
-        pheno_df = self.phenotype_df.copy()
+        pheno_df = (
+            self.phenotype_df.copy().reset_index().rename(columns={"index": "SampleID"})
+        )
         num_samples = len(pheno_df)
-        pheno_id_col = pheno_df.columns[0]
+        pheno_id_col = "SampleID"
         self.logger.info(f"Number of samples in phenotype data: {num_samples}")
+
+        pheno_df[pheno_id_col] = (
+            pheno_df[pheno_id_col].astype(str).str.strip().str.upper()
+        )
+
         serialized_data = {"phenotype": pheno_df.to_csv(index=False)}
 
         for i, omics_df in enumerate(self.omics_dfs, start=1):
             current_key = f"omics_{i}"
-            df = omics_df.copy()
+            df = omics_df.copy().reset_index().rename(columns={"index": "SampleID"})
 
-            if len(df) != num_samples:
-                raise ValueError(
-                    f"Mismatch in sample count for {current_key}: phenotype has {num_samples} rows, "
-                    f"but {current_key} has {len(df)} rows. Please align your data."
-                )
+            omics_id_col = "SampleID"
+            df[omics_id_col] = df[omics_id_col].astype(str).str.strip().str.upper()
 
-            omics_id_col = df.columns[0]
+            common_ids = set(pheno_df[pheno_id_col]).intersection(set(df[omics_id_col]))
+            self.logger.info(
+                f"Number of common samples in {current_key}: {len(common_ids)}"
+            )
 
-            if not df[omics_id_col].equals(pheno_df[pheno_id_col]):
-                self.logger.warning(
-                    f"Sample IDs in phenotype '{pheno_id_col}' and {current_key} '{omics_id_col}' do not match. "
-                    "Replacing with sequential IDs."
+            if len(common_ids) == 0:
+                raise ValueError(
+                    f"No overlapping sample IDs between phenotype and {current_key}."
                 )
 
-                df[omics_id_col] = range(1, num_samples + 1)
-                pheno_df[pheno_id_col] = range(1, num_samples + 1)
-
-            if df.isna().any().any():
-                self.logger.warning(f"NaN values found in {current_key}.")
-            if ((df == float("inf")) | (df == -float("inf"))).any().any():
-                self.logger.warning(f"Inf values found in {current_key}.")
-
+            pheno_df = pheno_df[pheno_df[pheno_id_col].isin(common_ids)]
+            df = df[df[omics_id_col].isin(common_ids)]
+            df = df.set_index(omics_id_col).loc[pheno_df[pheno_id_col]].reset_index()
             serialized_data[current_key] = df.to_csv(index=False)
 
         self.logger.info("Preprocessing checks completed successfully.")
         return serialized_data
 
-    def run_smccnet(self, serialized_data: Dict[str, Any]) -> str:
+    def run_smccnet(self, serialized_data: Dict[str, Any]) -> None:
         """
         Executes the SmCCNet R script by passing serialized data via standard input.
 
@@ -141,23 +141,11 @@ def run_smccnet(self, serialized_data: Dict[str, Any]) -> str:
 
             self.logger.debug(f"Executing command: {' '.join(command)}")
 
-            result = subprocess.run(
+            subprocess.run(
                 command, input=json_data, text=True, capture_output=True, check=True
             )
 
             self.logger.info("SmCCNet R script executed successfully.")
-            self.logger.debug(f"Raw R script stdout: {result.stdout}")
-            if result.stderr:
-                self.logger.warning(f"SmCCNet Warnings/Errors:\n{result.stderr}")
-
-            adjacency_json = result.stdout.strip()
-            try:
-                json.loads(adjacency_json)
-            except json.JSONDecodeError:
-                self.logger.error("Invalid JSON output from R script.")
-                raise ValueError("R script did not produce valid JSON output.")
-
-            return adjacency_json
 
         except subprocess.CalledProcessError as e:
             self.logger.error(f"R script execution failed: {e.stderr}")
@@ -206,22 +194,17 @@ def run(self) -> pd.DataFrame:
         try:
             self.logger.info("Starting SmCCNet Graph Generation Workflow.")
             serialized_data = self.preprocess_data()
-            adjacency_json = self.run_smccnet(serialized_data)
-
-            self.logger.info("SmCCNet JSON output received.")
-            adjacency_dict = json.loads(adjacency_json)
-            self.logger.info("type(adjacency_dict): %s", type(adjacency_dict))
-            self.logger.info("adjacency_dict.keys(): %s", adjacency_dict)
-            adjacency_matrix = pd.DataFrame(
-                data=adjacency_dict["data"],
-                index=adjacency_dict["index"],
-                columns=adjacency_dict["columns"],
+            self.run_smccnet(serialized_data)
+
+            self.logger.info("Loading adjacency matrix from CSV.")
+            adjacency_df = pd.read_csv(
+                f"{os.getcwd()}\\adjacencyMatrix.csv", index_col=0
             )
             self.logger.info(
-                "Adjacency matrix loaded with shape: %s", adjacency_matrix.shape
+                "Adjacency matrix loaded with shape: %s", adjacency_df.shape
             )
             self.logger.info("SmCCNet Graph Generation completed successfully.")
-            return adjacency_matrix
+            return adjacency_df
         except Exception as e:
             self.logger.error(f"Error in SmCCNet Graph Generation: {e}")
             raise
diff --git a/bioneuralnet/metrics/__init__.py b/bioneuralnet/metrics/__init__.py
new file mode 100644
index 0000000..3703e67
--- /dev/null
+++ b/bioneuralnet/metrics/__init__.py
@@ -0,0 +1,7 @@
+from .correlation import Correlation
+from .mse import MSE
+from .precision import Precision
+from .recall import Recall
+from .f1_score import F1Score
+
+__all__ = ["Correlation", "MSE", "Precision", "Recall", "F1Score"]
diff --git a/bioneuralnet/metrics/correlation.py b/bioneuralnet/metrics/correlation.py
new file mode 100644
index 0000000..474e8c4
--- /dev/null
+++ b/bioneuralnet/metrics/correlation.py
@@ -0,0 +1,32 @@
+import pandas as pd
+from ..utils.logger import get_logger
+
+
+class Correlation:
+    """
+    Computes the Pearson correlation coefficient between predictions and targets.
+    """
+
+    def __init__(self):
+        self.logger = get_logger(__name__)
+
+    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
+        """
+        Compute the Pearson correlation coefficient between predictions and targets.
+        Parameters
+        Returns
+        Raises
+        """
+        self.logger.info("Computing Pearson correlation coefficient.")
+
+        if predictions.empty or targets.empty:
+            self.logger.error("Predictions and targets must not be empty.")
+            raise ValueError("Predictions and targets must not be empty.")
+
+        if len(predictions) != len(targets):
+            self.logger.error("Predictions and targets must have the same length.")
+            raise ValueError("Predictions and targets must have the same length.")
+
+        correlation = predictions.corr(targets)
+        self.logger.info(f"Pearson correlation coefficient: {correlation}")
+        return correlation if not pd.isna(correlation) else 0.0
diff --git a/bioneuralnet/metrics/f1_score.py b/bioneuralnet/metrics/f1_score.py
new file mode 100644
index 0000000..2b5b1a9
--- /dev/null
+++ b/bioneuralnet/metrics/f1_score.py
@@ -0,0 +1,56 @@
+import pandas as pd
+from ..utils.logger import get_logger
+
+
+class F1Score:
+    """
+    F1 Score Metric Class.
+
+    Computes the F1 score between predictions and targets.
+    Suitable for classification tasks.
+    """
+
+    def __init__(self, average: str = "binary"):
+        """
+        Initialize the F1 Score metric.
+        """
+        self.logger = get_logger(__name__)
+        self.average = average
+
+    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
+        """
+        Compute the F1 score between predictions and targets.
+        Parameter
+        Returns
+        Raises
+        """
+        self.logger.info(f"Computing F1 Score with average='{self.average}'.")
+
+        if predictions.empty or targets.empty:
+            self.logger.error("Predictions and targets must not be empty.")
+            raise ValueError("Predictions and targets must not be empty.")
+
+        if len(predictions) != len(targets):
+            self.logger.error("Predictions and targets must have the same length.")
+            raise ValueError("Predictions and targets must have the same length.")
+
+        if self.average != "binary":
+            self.logger.error(
+                "Only 'binary' average is supported without scikit-learn."
+            )
+            raise NotImplementedError("Only 'binary' average is supported.")
+
+        tp = ((predictions == 1) & (targets == 1)).sum()
+        fp = ((predictions == 1) & (targets == 0)).sum()
+        fn = ((predictions == 0) & (targets == 1)).sum()
+
+        precision = float(tp) / float(tp + fp) if (tp + fp) > 0 else 0.0
+        recall = float(tp) / float(tp + fn) if (tp + fn) > 0 else 0.0
+
+        if precision + recall == 0:
+            f1 = 0.0
+        else:
+            f1 = 2.0 * (precision * recall) / (precision + recall)
+
+        self.logger.info(f"F1 Score: {f1}")
+        return f1
diff --git a/bioneuralnet/metrics/mse.py b/bioneuralnet/metrics/mse.py
new file mode 100644
index 0000000..3ecaa12
--- /dev/null
+++ b/bioneuralnet/metrics/mse.py
@@ -0,0 +1,32 @@
+import pandas as pd
+from ..utils.logger import get_logger
+
+
+class MSE:
+    """
+    Computes the mean squared error between predictions and targets.
+    """
+
+    def __init__(self):
+        self.logger = get_logger(__name__)
+
+    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
+        """
+        Compute the mean squared error between predictions and targets.
+        Parameters
+        Returns
+        Raises
+        """
+        self.logger.info("Computing Mean Squared Error (MSE).")
+
+        if predictions.empty or targets.empty:
+            self.logger.error("Predictions and targets must not be empty.")
+            raise ValueError("Predictions and targets must not be empty.")
+
+        if len(predictions) != len(targets):
+            self.logger.error("Predictions and targets must have the same length.")
+            raise ValueError("Predictions and targets must have the same length.")
+
+        mse = ((predictions - targets) ** 2).mean()
+        self.logger.info(f"Mean Squared Error (MSE): {mse}")
+        return mse
diff --git a/bioneuralnet/metrics/precision.py b/bioneuralnet/metrics/precision.py
new file mode 100644
index 0000000..180df27
--- /dev/null
+++ b/bioneuralnet/metrics/precision.py
@@ -0,0 +1,46 @@
+import pandas as pd
+from ..utils.logger import get_logger
+
+
+class Precision:
+    """
+    Computes the precision score between predictions and targets.
+    for classification tasks.
+    """
+
+    def __init__(self, average: str = "binary"):
+        """
+        Initialize the Precision metric.
+        """
+        self.logger = get_logger(__name__)
+        self.average = average
+
+    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
+        """
+        Compute the precision score between predictions and targets.
+        Parameters
+        Returns
+        Raises
+        """
+        self.logger.info(f"Computing Precision with average='{self.average}'.")
+
+        if predictions.empty or targets.empty:
+            self.logger.error("Predictions and targets must not be empty.")
+            raise ValueError("Predictions and targets must not be empty.")
+
+        if len(predictions) != len(targets):
+            self.logger.error("Predictions and targets must have the same length.")
+            raise ValueError("Predictions and targets must have the same length.")
+
+        if self.average != "binary":
+            self.logger.error(
+                "Only 'binary' average is supported without scikit-learn."
+            )
+            raise NotImplementedError("Only 'binary' average is supported.")
+
+        tp = ((predictions == 1) & (targets == 1)).sum()
+        fp = ((predictions == 1) & (targets == 0)).sum()
+
+        precision = float(tp) / float(tp + fp) if (tp + fp) > 0 else 0.0
+        self.logger.info(f"Precision Score: {precision}")
+        return precision
diff --git a/bioneuralnet/metrics/recall.py b/bioneuralnet/metrics/recall.py
new file mode 100644
index 0000000..ef16e57
--- /dev/null
+++ b/bioneuralnet/metrics/recall.py
@@ -0,0 +1,46 @@
+import pandas as pd
+from ..utils.logger import get_logger
+
+
+class Recall:
+    """
+    Computes the recall score between predictions and targets.
+    for classification tasks.
+    """
+
+    def __init__(self, average: str = "binary"):
+        """
+        Initialize the Recall metric.
+        """
+        self.logger = get_logger(__name__)
+        self.average = average
+
+    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
+        """
+        Compute the recall score between predictions and targets.
+        Parameters
+        Returns
+        Raises
+        """
+        self.logger.info(f"Computing Recall with average='{self.average}'.")
+
+        if predictions.empty or targets.empty:
+            self.logger.error("Predictions and targets must not be empty.")
+            raise ValueError("Predictions and targets must not be empty.")
+
+        if len(predictions) != len(targets):
+            self.logger.error("Predictions and targets must have the same length.")
+            raise ValueError("Predictions and targets must have the same length.")
+
+        if self.average != "binary":
+            self.logger.error(
+                "Only 'binary' average is supported without scikit-learn."
+            )
+            raise NotImplementedError("Only 'binary' average is supported.")
+
+        tp = ((predictions == 1) & (targets == 1)).sum()
+        fn = ((predictions == 0) & (targets == 1)).sum()
+
+        recall = float(tp) / float(tp + fn) if (tp + fn) > 0 else 0.0
+        self.logger.info(f"Recall Score: {recall}")
+        return recall
diff --git a/bioneuralnet/network_embedding/gnn_embedding.py b/bioneuralnet/network_embedding/gnn_embedding.py
index f32d84b..963b0ce 100644
--- a/bioneuralnet/network_embedding/gnn_embedding.py
+++ b/bioneuralnet/network_embedding/gnn_embedding.py
@@ -4,6 +4,7 @@
 import torch.optim as optim
 import pandas as pd
 import networkx as nx
+import numpy as np
 from torch_geometric.data import Data
 from torch_geometric.utils import from_networkx
 from .gnn_models import GCN, GAT, SAGE, GIN
@@ -13,14 +14,6 @@
 class GNNEmbedding:
     """
     GNNEmbedding Class for Generating Graph Neural Network (GNN) Based Embeddings.
-    -------------------------------------------------------------------------
-    1) X: For each node (omics feature), build a feature vector by correlating that feature
-       with each clinical variable.
-    2) Y: For each node, assign a label = correlation(node, phenotype).
-    3) Pass (X, Y) + adjacency to a GNN (GCN, GAT, SAGE, GIN), each returning a single float
-       per node for MSE regression.
-    4) Train with MSELoss over 'num_epochs'.
-    5) Return final node embeddings (penultimate layer).
     """
 
     def __init__(
@@ -28,7 +21,7 @@ def __init__(
         adjacency_matrix: pd.DataFrame,
         omics_data: pd.DataFrame,
         phenotype_data: pd.DataFrame,
-        clinical_data: pd.DataFrame,
+        clinical_data: Optional[pd.DataFrame] = None,
         phenotype_col: str = "phenotype",
         model_type: str = "GAT",
         hidden_dim: int = 64,
@@ -38,45 +31,46 @@ def __init__(
         lr: float = 1e-3,
         weight_decay: float = 1e-4,
         gpu: bool = False,
+        random_feature_dim: int = 16,
+        seed: Optional[int] = None,
     ):
         """
+        Initializes the GNNEmbedding instance.
+
         Parameters
-        ----------
         adjacency_matrix : pd.DataFrame
-            NxN adjacency matrix for nodes (omics features).
         omics_data : pd.DataFrame
-            (samples x features) table, columns must match adjacency index.
         phenotype_data : pd.DataFrame
-            (samples x 1) table containing 'phenotype_col' for each sample.
-        clinical_data : pd.DataFrame
-            (samples x clinical_vars) table.
-        phenotype_col : str
-            Column in phenotype_data used for correlation-based node label.
-        model_type : str
-            One of {"GCN", "GAT", "SAGE", "GIN"}.
-        hidden_dim : int
-            Hidden dimension for the GNN layers.
-        layer_num : int
-            Number of GNN layers.
-        dropout : bool
-            Whether to apply dropout in the GNN.
-        num_epochs : int
-            Number of epochs for MSE training.
-        lr : float
-            Learning rate.
-        weight_decay : float
-            Weight decay (L2 regularization).
-        gpu : bool
-            Whether to use GPU if available.
+        clinical_data : Optional[pd.DataFrame], default=None
+        phenotype_col : str, optional
+        model_type : str, optional
+        hidden_dim : int, optional
+        layer_num : int, optional
+        dropout : bool, optional
+        num_epochs : int, optional
+        lr : float, optional
+        weight_decay : float, optional
+        gpu : bool, optional
+        random_feature_dim : int, optional
+        seed : Optional[int], default=None
         """
+        self.logger = get_logger(__name__)
+        if seed is not None:
+            torch.manual_seed(seed)
+            np.random.seed(seed)
+
+        # input validation
         if adjacency_matrix.empty:
             raise ValueError("Adjacency matrix cannot be empty.")
         if omics_data.empty:
             raise ValueError("Omics data cannot be empty.")
         if phenotype_data.empty or phenotype_col not in phenotype_data.columns:
             raise ValueError(f"Phenotype data must have column '{phenotype_col}'.")
-        if clinical_data.empty:
-            raise ValueError("Clinical data cannot be empty.")
+        if clinical_data is not None and clinical_data.empty:
+            self.logger.warning(
+                "Clinical data provided is empty ... using random features."
+            )
+            clinical_data = None
 
         self.adjacency_matrix = adjacency_matrix
         self.omics_data = omics_data
@@ -91,8 +85,8 @@ def __init__(
         self.num_epochs = num_epochs
         self.lr = lr
         self.weight_decay = weight_decay
+        self.random_feature_dim = random_feature_dim
 
-        self.logger = get_logger(__name__)
         self.device = torch.device(
             "cuda" if gpu and torch.cuda.is_available() else "cpu"
         )
@@ -100,84 +94,127 @@ def __init__(
             f"Initialized GNNEmbedding for regression. device={self.device}"
         )
 
-    def run(self) -> Dict[str, torch.Tensor]:
+        self.model = None
+        self.data = None
+        self.embeddings = None
+
+    def fit(self) -> None:
+        """
+        Trains the GNN model using the provided data.
         """
-        Generate GNN-based embeddings from the provided adjacency matrix and node features.
+        self.logger.info("Starting training process.")
+        try:
+            node_features = self._prepare_node_features()
+            node_labels = self._prepare_node_labels()
+            self.data = self._build_pyg_data(node_features, node_labels)
+            self.model = self._initialize_gnn_model().to(self.device)
+            self._train_gnn(self.model, self.data)
+            self.logger.info("Training completed successfully.")
+        except Exception as e:
+            self.logger.error(f"Error during training: {e}")
+            raise
 
-        **Returns**: Dict[str, torch.Tensor]
-            - A dictionary where keys are graph names (e.g., 'graph') and values are PyTorch tensors of shape
-              `(num_nodes, embedding_dim)` containing the node embeddings.
+    def embed(self) -> torch.Tensor:
+        """
+        Generates and retrieves node embeddings from the trained GNN model.
 
-        **Raises**:
-            - **ValueError**: If node features cannot be computed or if required nodes are missing.
-            - **Exception**: For any unforeseen errors during node feature preparation, model inference, or embedding generation.
+        Returns:
+            torch.Tensor
 
-        **Notes**:
-            - Ensure the adjacency matrix aligns with nodes in omics_data.
-            - Node features are built by correlating each feature (column) with clinical_data.
-            - Node labels are built by correlating each feature with `phenotype_col`.
+        Raises:
+            ValueError
         """
-        self.logger.info("Running GNN Embedding process for node-level regression.")
+        self.logger.info("Generating node embeddings.")
+        if self.model is None or self.data is None:
+            self.logger.error(
+                "Model has not been trained. Call 'fit()' before 'embed()'."
+            )
+            raise ValueError(
+                "Model has not been trained. Call 'fit()' before 'embed()'."
+            )
 
-        node_features = self._prepare_node_features()
-        node_labels = self._prepare_node_labels()
-        data = self._build_pyg_data(node_features, node_labels)
-        model = self._initialize_gnn_model().to(self.device)
-        self._train_gnn(model, data)
-        embeddings = self._generate_embeddings(model, data)
-        return {"graph": embeddings}
+        try:
+            self.embeddings = self._generate_embeddings(self.model, self.data)
+            self.logger.info("Node embeddings generated successfully.")
+            return self.embeddings
+        except Exception as e:
+            self.logger.error(f"Error during embedding generation: {e}")
+            raise
 
     def _prepare_node_features(self) -> pd.DataFrame:
         """
-        Build node features by correlating each omics feature with each clinical variable.
+        Build node features by correlating each omics feature with each clinical variable
+        (if clinical data is provided). Otherwise, initialize node features randomly.
 
-        Returns
-        -------
-        pd.DataFrame
-            Shape (num_nodes, num_clinical_vars), each entry is correlation(feature_i, clinical_var_j).
+        Returns:
+            pd.DataFrame
         """
-        self.logger.info(
-            "Preparing node features by correlating each omic with clinical vars."
-        )
-        common_samples = self.omics_data.index.intersection(
-            self.phenotype_data.index
-        ).intersection(self.clinical_data.index)
-        if len(common_samples) == 0:
-            raise ValueError("No common samples among omics, phenotype, clinical.")
-
-        omics_filtered = self.omics_data.loc[common_samples]
-        clinical_filtered = self.clinical_data.loc[common_samples]
+        self.logger.info("Preparing node features.")
         node_names = self.adjacency_matrix.index.tolist()
 
-        node_features_list = []
-        clinical_cols = clinical_filtered.columns.tolist()
-        for node in node_names:
-            if node not in omics_filtered.columns:
-                raise ValueError(f"Node {node} not found in omics_data.")
-            corr_vector = []
-            for cvar in clinical_cols:
-                corr_val = omics_filtered[node].corr(clinical_filtered[cvar])
-                corr_vector.append(corr_val)
-            node_features_list.append(corr_vector)
-
-        node_features_df = pd.DataFrame(
-            node_features_list, index=node_names, columns=clinical_cols
-        ).fillna(0.0)
-        return node_features_df
+        if self.clinical_data is not None:
+            self.logger.info(
+                "Clinical data provided. Preparing node features based on correlations."
+            )
+            common_samples = self.omics_data.index.intersection(
+                self.phenotype_data.index
+            ).intersection(self.clinical_data.index)
+            if len(common_samples) == 0:
+                raise ValueError(
+                    "No common samples among omics, phenotype, and clinical data."
+                )
+
+            omics_filtered = self.omics_data.loc[common_samples]
+            clinical_filtered = self.clinical_data.loc[common_samples]
+            clinical_cols = clinical_filtered.columns.tolist()
+
+            node_features_list = []
+            for node in node_names:
+                if node not in omics_filtered.columns:
+                    raise ValueError(f"Node '{node}' not found in omics_data.")
+                corr_vector = []
+                for cvar in clinical_cols:
+                    corr_val = omics_filtered[node].corr(clinical_filtered[cvar])
+                    corr_vector.append(corr_val if not pd.isna(corr_val) else 0.0)
+                node_features_list.append(corr_vector)
+
+            node_features_df = pd.DataFrame(
+                node_features_list, index=node_names, columns=clinical_cols
+            ).fillna(0.0)
+            self.logger.info("Node features prepared based on clinical data.")
+            return node_features_df
+
+        else:
+            self.logger.info(
+                "No clinical data provided. Initializing node features randomly."
+            )
+            num_nodes = len(node_names)
+            num_features = self.random_feature_dim
+            random_features = np.random.rand(num_nodes, num_features)
+            node_features_df = pd.DataFrame(
+                random_features,
+                index=node_names,
+                columns=[f"RandomFeat_{i+1}" for i in range(num_features)],
+            )
+            self.logger.info(
+                f"Random node features initialized with shape {node_features_df.shape}."
+            )
+            return node_features_df
 
     def _prepare_node_labels(self) -> pd.Series:
         """
         Build node labels by correlating each omics feature with the specified phenotype column.
 
-        Returns
-        -------
-        pd.Series
-            Index=node, value=correlation(feature, phenotype).
+        Returns:
+            pd.Series
         """
         self.logger.info(
-            f"Preparing node labels from correlation with phenotype_col='{self.phenotype_col}'."
+            f"Preparing node labels by correlating each omics feature with phenotype column '{self.phenotype_col}'."
         )
         common_samples = self.omics_data.index.intersection(self.phenotype_data.index)
+        if len(common_samples) == 0:
+            raise ValueError("No common samples between omics data and phenotype data.")
+
         omics_filtered = self.omics_data.loc[common_samples]
         phen_filtered = self.phenotype_data.loc[common_samples, self.phenotype_col]
 
@@ -185,11 +222,12 @@ def _prepare_node_labels(self) -> pd.Series:
         node_names = self.adjacency_matrix.index.tolist()
         for node in node_names:
             if node not in omics_filtered.columns:
-                raise ValueError(f"Node {node} not in omics_data columns.")
+                raise ValueError(f"Node '{node}' not found in omics_data columns.")
             corr_val = omics_filtered[node].corr(phen_filtered)
-            labels_dict[node] = corr_val
+            labels_dict[node] = corr_val if not pd.isna(corr_val) else 0.0
 
         labels_series = pd.Series(labels_dict, index=node_names).fillna(0.0)
+        self.logger.info("Node labels prepared successfully.")
         return labels_series
 
     def _build_pyg_data(
@@ -197,16 +235,15 @@ def _build_pyg_data(
     ) -> Data:
         """
         Construct a PyTorch Geometric Data object:
-         - data.x = node_features
-         - data.y = node_labels
-         - data.edge_index from adjacency
 
-        Returns
-        -------
-        Data
+        - data.x = node_features
+        - data.y = node_labels
+        - data.edge_index from adjacency
+
+        Returns:
             PyG Data object with x, y, edge_index.
         """
-        self.logger.info("Constructing PyG Data (edge_index, data.x, data.y).")
+        self.logger.info("Constructing PyTorch Geometric Data object.")
         G = nx.from_pandas_adjacency(self.adjacency_matrix)
         node_mapping = {name: i for i, name in enumerate(node_features.index)}
         G = nx.relabel_nodes(G, node_mapping)
@@ -215,43 +252,48 @@ def _build_pyg_data(
         node_order = list(node_features.index)
         data.x = torch.tensor(node_features.loc[node_order].values, dtype=torch.float)
         data.y = torch.tensor(node_labels.loc[node_order].values, dtype=torch.float)
+        self.logger.info("PyTorch Geometric Data object constructed successfully.")
         return data
 
     def _initialize_gnn_model(self) -> nn.Module:
         """
-        Create a GNN model (GCN, GAT, SAGE, GIN) for node-level regression.
+        Initialize the GNN model based on the specified type.
+
+        Returns:
+            nn.Module
 
-        Returns
-        -------
-        nn.Module
         """
         self.logger.info(
-            f"Initializing GNN of type {self.model_type}, hidden_dim={self.hidden_dim}"
+            f"Initializing GNN model of type '{self.model_type}' with hidden_dim={self.hidden_dim} and layer_num={self.layer_num}."
         )
-        # node_features width = num_clinical_vars
-        input_dim = self.clinical_data.shape[1]
-        if self.model_type == "GCN":
+
+        if self.data is None or not hasattr(self.data, "x") or self.data.x is None:
+            raise ValueError("Data is not initialized or is missing the 'x' attribute.")
+
+        input_dim = self.data.x.shape[1]
+
+        if self.model_type.upper() == "GCN":
             return GCN(
                 input_dim=input_dim,
                 hidden_dim=self.hidden_dim,
                 layer_num=self.layer_num,
                 dropout=self.dropout,
             )
-        elif self.model_type == "GAT":
+        elif self.model_type.upper() == "GAT":
             return GAT(
                 input_dim=input_dim,
                 hidden_dim=self.hidden_dim,
                 layer_num=self.layer_num,
                 dropout=self.dropout,
             )
-        elif self.model_type == "SAGE":
+        elif self.model_type.upper() == "SAGE":
             return SAGE(
                 input_dim=input_dim,
                 hidden_dim=self.hidden_dim,
                 layer_num=self.layer_num,
                 dropout=self.dropout,
             )
-        elif self.model_type == "GIN":
+        elif self.model_type.upper() == "GIN":
             return GIN(
                 input_dim=input_dim,
                 hidden_dim=self.hidden_dim,
@@ -259,13 +301,14 @@ def _initialize_gnn_model(self) -> nn.Module:
                 dropout=self.dropout,
             )
         else:
+            self.logger.error(f"Unsupported model_type: {self.model_type}")
             raise ValueError(f"Unsupported model_type: {self.model_type}")
 
     def _train_gnn(self, model: nn.Module, data: Data) -> None:
         """
-        Train the GNN model (MSE regression). Each node's label is correlation in [-1..+1].
+        Train the GNN model using MSE loss.
         """
-        self.logger.info("Training GNN for regression using MSELoss.")
+        self.logger.info("Starting GNN training process.")
         data = data.to(self.device)
         model.to(self.device)
         model.train()
@@ -277,31 +320,29 @@ def _train_gnn(self, model: nn.Module, data: Data) -> None:
             weight_decay=self.weight_decay,
         )
 
-        for epoch in range(self.num_epochs):
+        for epoch in range(1, self.num_epochs + 1):
             optimizer.zero_grad()
-            # [num_nodes, 1]
             out = model(data)
-            # [num_nodes]
             out = out.view(-1)
             loss = criterion(out, data.y)
             loss.backward()
             optimizer.step()
 
-            if (epoch + 1) % 10 == 0 or epoch == 0:
+            if epoch % 10 == 0 or epoch == 1:
                 self.logger.info(
-                    f"Epoch [{epoch+1}/{self.num_epochs}], MSE Loss: {loss.item():.4f}"
+                    f"Epoch [{epoch}/{self.num_epochs}], MSE Loss: {loss.item():.4f}"
                 )
 
+        self.logger.info("GNN training process completed.")
+
     def _generate_embeddings(self, model: nn.Module, data: Data) -> torch.Tensor:
         """
-        Get node embeddings from the penultimate layer (hidden_dim) after training.
+        Retrieve node embeddings from the penultimate layer of the trained GNN model.
 
-        Returns
-        -------
-        torch.Tensor
-            Shape [num_nodes, hidden_dim].
+        Returns:
+            torch.Tensor
         """
-        self.logger.info("Generating final node embeddings (penultimate layer).")
+        self.logger.info("Generating node embeddings from the trained GNN model.")
         model.eval()
         data = data.to(self.device)
         with torch.no_grad():
diff --git a/bioneuralnet/network_embedding/gnn_models.py b/bioneuralnet/network_embedding/gnn_models.py
index 2e8e466..f3d9b23 100644
--- a/bioneuralnet/network_embedding/gnn_models.py
+++ b/bioneuralnet/network_embedding/gnn_models.py
@@ -5,52 +5,38 @@
 
 class GCN(nn.Module):
     """
-    GCN:
-      - Uses GCNConv layers to transform node features.
-      - Final layer is a simple linear that outputs 1 dimension per node (for MSE regression).
-      - get_embeddings(data) returns the penultimate layer's node embeddings.
+    GCN with optional final regression layer.
     """
 
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True):
+    def __init__(
+        self, input_dim, hidden_dim, layer_num=2, dropout=True, final_layer="regression"
+    ):
         super().__init__()
         self.convs = nn.ModuleList()
         self.convs.append(GCNConv(input_dim, hidden_dim))
         for _ in range(layer_num - 1):
             self.convs.append(GCNConv(hidden_dim, hidden_dim))
         self.dropout = dropout
+        self.final_layer = final_layer
 
-        # Final linear: from hidden_dim -> 1
-        self.regressor = nn.Linear(hidden_dim, 1)
+        if self.final_layer == "regression":
+            self.regressor = nn.Linear(hidden_dim, 1)
+        else:
+            self.regressor = None
 
     def forward(self, data):
-        """
-        Forward pass for node-level regression.
-
-        Parameters
-        ----------
-        data : torch_geometric.data.Data
-            PyG Data object with attributes:
-            - data.x : node features
-            - data.edge_index : edge connectivity
-
-        Returns
-        -------
-        torch.Tensor
-            Shape [num_nodes, 1], a single float per node for regression.
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
             x = F.relu(x)
             if self.dropout:
                 x = F.dropout(x, p=0.5, training=self.training)
-        out = self.regressor(x)  # shape [num_nodes, 1]
-        return out
+
+        if self.final_layer == "regression" and self.regressor is not None:
+            x = self.regressor(x)
+        return x
 
     def get_embeddings(self, data):
-        """
-        Returns the penultimate node embeddings (shape [num_nodes, hidden_dim]).
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
@@ -62,43 +48,45 @@ def get_embeddings(self, data):
 
 class GAT(nn.Module):
     """
-    GAT:
-      - Uses GATConv layers to transform node features.
-      - Final layer outputs 1 dimension for regression.
-      - get_embeddings(data) returns penultimate embeddings.
+    GAT with optional final regression layer.
     """
 
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True, heads=1):
+    def __init__(
+        self,
+        input_dim,
+        hidden_dim,
+        layer_num=2,
+        dropout=True,
+        heads=1,
+        final_layer="regression",
+    ):
         super().__init__()
         self.convs = nn.ModuleList()
         self.convs.append(GATConv(input_dim, hidden_dim, heads=heads))
         for _ in range(layer_num - 1):
             self.convs.append(GATConv(hidden_dim * heads, hidden_dim, heads=heads))
         self.dropout = dropout
-        self.regressor = nn.Linear(hidden_dim * heads, 1)
+        self.final_layer = final_layer
+        self.heads = heads
+
+        if self.final_layer == "regression":
+            self.regressor = nn.Linear(hidden_dim * heads, 1)
+        else:
+            self.regressor = None
 
     def forward(self, data):
-        """
-        Forward pass for node-level regression.
-
-        Returns
-        -------
-        torch.Tensor
-            [num_nodes, 1], single float per node.
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
             x = F.elu(x)
             if self.dropout:
                 x = F.dropout(x, p=0.5, training=self.training)
-        out = self.regressor(x)
-        return out
+
+        if self.final_layer == "regression" and self.regressor is not None:
+            x = self.regressor(x)
+        return x
 
     def get_embeddings(self, data):
-        """
-        Return penultimate embeddings: [num_nodes, hidden_dim * heads].
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
@@ -110,43 +98,38 @@ def get_embeddings(self, data):
 
 class SAGE(nn.Module):
     """
-    SAGE:
-      - Uses SAGEConv layers to transform node features.
-      - Final layer outputs 1 dimension for regression.
-      - get_embeddings(data) returns penultimate embeddings.
+    GraphSAGE with optional final regression layer.
     """
 
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True):
+    def __init__(
+        self, input_dim, hidden_dim, layer_num=2, dropout=True, final_layer="regression"
+    ):
         super().__init__()
         self.convs = nn.ModuleList()
         self.convs.append(SAGEConv(input_dim, hidden_dim))
         for _ in range(layer_num - 1):
             self.convs.append(SAGEConv(hidden_dim, hidden_dim))
         self.dropout = dropout
-        self.regressor = nn.Linear(hidden_dim, 1)
+        self.final_layer = final_layer
+
+        if self.final_layer == "regression":
+            self.regressor = nn.Linear(hidden_dim, 1)
+        else:
+            self.regressor = None
 
     def forward(self, data):
-        """
-        Forward pass for node-level regression.
-
-        Returns
-        -------
-        torch.Tensor
-            [num_nodes, 1]
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
             x = F.relu(x)
             if self.dropout:
                 x = F.dropout(x, p=0.5, training=self.training)
-        out = self.regressor(x)
-        return out
+
+        if self.final_layer == "regression" and self.regressor is not None:
+            x = self.regressor(x)
+        return x
 
     def get_embeddings(self, data):
-        """
-        Return penultimate embeddings: [num_nodes, hidden_dim].
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
@@ -158,13 +141,12 @@ def get_embeddings(self, data):
 
 class GIN(nn.Module):
     """
-    GIN:
-      - Uses GINConv layers.
-      - Final layer outputs 1 dimension for regression.
-      - get_embeddings(data) returns penultimate embeddings.
+    GIN with optional final regression layer.
     """
 
-    def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True):
+    def __init__(
+        self, input_dim, hidden_dim, layer_num=2, dropout=True, final_layer="regression"
+    ):
         super().__init__()
         self.convs = nn.ModuleList()
         for i in range(layer_num):
@@ -174,31 +156,27 @@ def __init__(self, input_dim, hidden_dim, layer_num=2, dropout=True):
                 nn.Linear(hidden_dim, hidden_dim),
             )
             self.convs.append(GINConv(nn_module))
+
         self.dropout = dropout
-        self.regressor = nn.Linear(hidden_dim, 1)
+        self.final_layer = final_layer
+        if self.final_layer == "regression":
+            self.regressor = nn.Linear(hidden_dim, 1)
+        else:
+            self.regressor = None
 
     def forward(self, data):
-        """
-        Forward pass for node-level regression.
-
-        Returns
-        -------
-        torch.Tensor
-            [num_nodes, 1]
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
             x = F.relu(x)
             if self.dropout:
                 x = F.dropout(x, p=0.5, training=self.training)
-        out = self.regressor(x)
-        return out
+
+        if self.final_layer == "regression" and self.regressor is not None:
+            x = self.regressor(x)
+        return x
 
     def get_embeddings(self, data):
-        """
-        Return penultimate embeddings: [num_nodes, hidden_dim].
-        """
         x, edge_index = data.x, data.edge_index
         for conv in self.convs:
             x = conv(x, edge_index)
diff --git a/bioneuralnet/subject_representation/subject_representation.py b/bioneuralnet/subject_representation/subject_representation.py
index c2142cd..5589967 100644
--- a/bioneuralnet/subject_representation/subject_representation.py
+++ b/bioneuralnet/subject_representation/subject_representation.py
@@ -1,17 +1,13 @@
 import pandas as pd
 from sklearn.decomposition import PCA
+from typing import Optional
 from ..utils.logger import get_logger
 from ..network_embedding import GNNEmbedding
 
 
 class GraphEmbedding:
     """
-    GraphEmbedding Class for Integrating Network Embeddings into Omics Data.
-
-    This class takes already loaded data structures and applies network embeddings
-    to enhance subject representations. It can either use precomputed embeddings or
-    train a new GNN (regression-based or otherwise) through ``GNNEmbedding``.
-
+    GraphEmbedding Class for Integrating Network Embeddings into Omics Data..
     """
 
     def __init__(
@@ -19,41 +15,54 @@ def __init__(
         adjacency_matrix: pd.DataFrame,
         omics_data: pd.DataFrame,
         phenotype_data: pd.DataFrame,
-        clinical_data: pd.DataFrame,
-        embeddings: pd.DataFrame = None,
+        clinical_data: Optional[pd.DataFrame] = None,
+        embeddings: Optional[pd.DataFrame] = None,
         reduce_method: str = "PCA",
     ):
         """
-        Parameters
-        ----------
-        adjacency_matrix : pd.DataFrame
-            NxN adjacency matrix for omics features (nodes).
-        omics_data : pd.DataFrame
-            (samples x features) table of omics data.
-        phenotype_data : pd.DataFrame
-            (samples x some_phenotype) table, must contain `phenotype_col`.
-        clinical_data : pd.DataFrame
-            (samples x clinical_vars) table.
-        embeddings : pd.DataFrame, optional
-            Precomputed node embeddings to skip GNN if not None or empty.
+        Initializes the GraphEmbedding instance.
+
+        Parameters:
+            adjacency_matrix : pd.DataFrame
+            omics_data : pd.DataFrame
+            phenotype_data : pd.DataFrame
+            clinical_data : Optional[pd.DataFrame], default=None
+            embeddings : Optional[pd.DataFrame], default=None
+            reduce_method : str, optional
         """
+        self.logger = get_logger(__name__)
+        self.logger.info("Initializing GraphEmbedding with provided data inputs.")
+
         if adjacency_matrix is None or adjacency_matrix.empty:
             raise ValueError("Adjacency matrix is required and cannot be empty.")
         if omics_data is None or omics_data.empty:
             raise ValueError("Omics data must be non-empty.")
-        if clinical_data is None or clinical_data.empty:
-            raise ValueError("Clinical data is required and cannot be empty.")
         if phenotype_data is None or phenotype_data.empty:
             raise ValueError("Phenotype data is required and cannot be empty.")
 
+        if clinical_data is not None and clinical_data.empty:
+            self.logger.warning(
+                "Clinical data provided is empty. Node features will be initialized randomly."
+            )
+            clinical_data = None
+
         if embeddings is None or embeddings.empty:
-            self.logger = get_logger(__name__)
+            self.embedding_method = "GNNs"
             self.logger.info(
-                "No precomputed embeddings -> defaulting to GNN-based approach."
+                "No precomputed embeddings provided. Defaulting to GNN-based embedding generation."
             )
-            self.embedding_method = "GNNs"
         else:
             self.embedding_method = "precomputed"
+            if not isinstance(embeddings, pd.DataFrame):
+                raise ValueError("Embeddings must be provided as a pandas DataFrame.")
+            missing_nodes = set(adjacency_matrix.index) - set(embeddings.index)
+            if missing_nodes:
+                raise ValueError(
+                    f"Provided embeddings are missing nodes: {missing_nodes}"
+                )
+            self.logger.info(
+                "Precomputed embeddings provided. Skipping GNN-based embedding generation."
+            )
 
         self.adjacency_matrix = adjacency_matrix
         self.omics_data = omics_data
@@ -62,62 +71,56 @@ def __init__(
         self.embeddings = embeddings
         self.reduce_method = reduce_method
 
-        self.logger = get_logger(__name__)
-        self.logger.info("Initialized GraphEmbedding with direct data inputs.")
-
     def run(self) -> pd.DataFrame:
         """
         Main pipeline:
-          1) Generate (or load) node embeddings
-          2) Reduce them to 1D with PCA
-          3) Integrate each node's PCA value into the subject-level omics data
+            - Generate (or load) node embeddings.
+            - Reduce them to 1D with the specified reduction method.
+            - Integrate each node's reduced embedding value into the subject-level omics data.
 
-        **Returns**:
+        Returns:
             pd.DataFrame
-                Enhanced omics data, weighted by the node embeddings or PCA of embeddings.
-
-        **Raises**:
-            - **ValueError**: If embeddings are empty or an error occurs in PCA/integration.
-            - **Exception**: For any unforeseen errors encountered.
 
-        **Notes**:
-            - If ``self.embeddings`` is provided, we skip training.
-            - Otherwise, we instantiate a ``GNNEmbedding`` object which trains a node-level
-              regression model if ``model_type`` is one of {"GCN", "GAT", "SAGE", "GIN"}.
+        Raises:
+            ValueError
+            Exception
         """
-        self.logger.info("Running Subject Representation workflow.")
+        self.logger.info("Starting Subject Representation workflow.")
         try:
             embeddings_df = self.generate_embeddings()
             node_embedding_values = self.reduce_embeddings(embeddings_df)
             enhanced_omics_data = self.integrate_embeddings(node_embedding_values)
+
+            self.logger.info("Subject Representation workflow completed successfully.")
             return enhanced_omics_data
 
         except Exception as e:
-            self.logger.error(f"Error in Subject Representation: {e}")
+            self.logger.error(f"Error in Subject Representation workflow: {e}")
             raise
 
     def generate_embeddings(self) -> pd.DataFrame:
         """
         Generate or retrieve node embeddings.
 
-        If embeddings are provided (precomputed), return them directly.
-        Otherwise, create a GNNEmbedding to do correlation-based node feature/label
-        and train the GNN for MSE regression.
-
-        **Returns**:
+        Returns:
             pd.DataFrame
-                Node embeddings of shape [num_nodes, embedding_dim].
+        Raises:
+            ValueError
         """
-        self.logger.info(f"Generating embeddings with method='{self.embedding_method}'")
+        self.logger.info(
+            f"Generating embeddings using method='{self.embedding_method}'."
+        )
 
         if self.embedding_method == "precomputed":
-            if not isinstance(self.embeddings, pd.DataFrame):
-                raise ValueError("Embeddings must be a pandas DataFrame.")
-            if self.embeddings.empty:
-                raise ValueError("Provided embeddings are empty.")
-            return self.embeddings
+            self.logger.info("Using precomputed embeddings.")
+            # making sure embeddings are not None, or precommit will fail
+            if self.embeddings is None:
+                raise ValueError("Embeddings are None.")
+            return self.embeddings.copy()
 
         else:
+            self.logger.info("Generating embeddings using GNNEmbedding.")
+            # for the other parameters it will just use default values
             gnn_embedder = GNNEmbedding(
                 adjacency_matrix=self.adjacency_matrix,
                 omics_data=self.omics_data,
@@ -125,75 +128,94 @@ def generate_embeddings(self) -> pd.DataFrame:
                 clinical_data=self.clinical_data,
             )
 
-        embeddings_dict = gnn_embedder.run()
-        embeddings_tensor = embeddings_dict["graph"]
+            gnn_embedder.fit()
+            embeddings_tensor = gnn_embedder.embed()
 
-        node_names = self.adjacency_matrix.index
-        embeddings_df = pd.DataFrame(embeddings_tensor.numpy(), index=node_names)
-        return embeddings_df
+            node_names = self.adjacency_matrix.index.tolist()
+            embeddings_df = pd.DataFrame(
+                embeddings_tensor.numpy(),
+                index=node_names,
+                columns=[f"Embed_{i+1}" for i in range(embeddings_tensor.shape[1])],
+            )
+            self.logger.info(
+                f"Generated GNN-based embeddings with shape {embeddings_df.shape}."
+            )
+            return embeddings_df
 
     def reduce_embeddings(self, embeddings: pd.DataFrame) -> pd.Series:
         """
-        Reduce embeddings to a single dimension per node using specified method.
-
-        **Parameters**:
-            embeddings: pd.DataFrame
-                A DataFrame containing the embeddings to be reduced.
-            method: str, optional
-                The dimensionality reduction method to use. Options:
-                - "pca" (default): Reduce using the first principal component via PCA.
-                - "average": Compute the average of all features.
-                - "maximum": Compute the maximum value of all features.
-
-        **Returns**:
+        Reduce embeddings to a single dimension per node using the specified method.
+
+        Parameters:
+            embeddings : pd.DataFrame
+
+        Returns:
             pd.Series
-                A Series indexed by node, containing the 1D embedding.
 
-        **Raises**:
-            ValueError:
-                If the embeddings DataFrame is empty or if an unsupported method is provided.
+        Raises:
+            ValueError
         """
+        self.logger.info(
+            f"Reducing embeddings to 1D using method='{self.reduce_method}'."
+        )
+
         if embeddings.empty:
             raise ValueError("Embeddings DataFrame is empty.")
 
-        if self.reduce_method == "PCA":
-            self.logger.info("Reducing node embeddings to 1D via PCA.")
-            pca = PCA(n_components=1)
+        if self.reduce_method.upper() == "PCA":
+            self.logger.info("Applying PCA to reduce embeddings to 1D.")
+            pca = PCA(n_components=1, random_state=42)
             principal_components = pca.fit_transform(embeddings)
             reduced_embedding = pd.Series(
                 principal_components.flatten(), index=embeddings.index, name="PC1"
             )
-        elif self.reduce_method == "AVG":
-            self.logger.info("Reducing node embeddings to 1D via averaging.")
+            self.logger.info("PCA reduction completed.")
+        elif self.reduce_method.upper() == "AVG":
+            self.logger.info("Calculating average of embedding dimensions.")
             reduced_embedding = embeddings.mean(axis=1)
-        elif self.reduce_method == "MAX":
-            self.logger.info("Reducing node embeddings to 1D via maximum.")
+            reduced_embedding.name = "Avg_Embed"
+            self.logger.info("Average reduction completed.")
+        elif self.reduce_method.upper() == "MAX":
+            self.logger.info("Calculating maximum of embedding dimensions.")
             reduced_embedding = embeddings.max(axis=1)
+            reduced_embedding.name = "Max_Embed"
+            self.logger.info("Maximum reduction completed.")
         else:
+            self.logger.error(f"Unsupported reduction method: {self.reduce_method}")
             raise ValueError(f"Unsupported reduction method: {self.reduce_method}")
 
         return reduced_embedding
 
     def integrate_embeddings(self, node_embedding_values: pd.Series) -> pd.DataFrame:
         """
-        Multiply each omics feature in self.omics_data by the PCA scalar for that node.
-
-        **Returns**:
-            pd.DataFrame
-                Enhanced omics data with integrated embeddings.
+        Integrate the reduced node embeddings into the subject-level omics data.
         """
-        self.logger.info("Integrating node embeddings into subject-level omics.")
-        modified_omics = self.omics_data.copy()
-
-        feature_cols = modified_omics.columns
-        missing_nodes = set(feature_cols) - set(node_embedding_values.index)
-        if missing_nodes:
-            self.logger.warning(f"Some features have no embeddings: {missing_nodes}")
-
-        for node in feature_cols:
-            if node in node_embedding_values.index:
-                modified_omics[node] = (
-                    modified_omics[node] * node_embedding_values[node]
+        self.logger.info(
+            "Integrating reduced node embeddings into subject-level omics data."
+        )
+
+        try:
+            feature_cols = self.omics_data.columns
+            missing_nodes = set(feature_cols) - set(node_embedding_values.index)
+            if missing_nodes:
+                self.logger.warning(
+                    f"Some omics features have no corresponding embeddings: {missing_nodes}"
                 )
 
-        return modified_omics
+            enhanced_omics = self.omics_data.copy()
+            for node in feature_cols:
+                if node in node_embedding_values.index:
+                    enhanced_omics[node] = (
+                        enhanced_omics[node] * node_embedding_values[node]
+                    )
+                else:
+                    self.logger.warning(
+                        f"No embedding found for feature '{node}'. Skipping integration for this feature."
+                    )
+
+            self.logger.info("Integration of embeddings completed successfully.")
+            return enhanced_omics
+
+        except Exception as e:
+            self.logger.error(f"Error during integration of embeddings: {e}")
+            raise
diff --git a/docs/source/clustering.rst b/docs/source/clustering.rst
new file mode 100644
index 0000000..e563594
--- /dev/null
+++ b/docs/source/clustering.rst
@@ -0,0 +1,6 @@
+.. _clustering:
+
+Clustering for Multi-Omics
+==========================
+
+**Coming soon**
diff --git a/docs/source/examples/example_1.py b/docs/source/examples/example_1.py
index 09cb8f1..afeebbd 100644
--- a/docs/source/examples/example_1.py
+++ b/docs/source/examples/example_1.py
@@ -58,12 +58,11 @@ def run_smccnet_workflow(
             lr=1e-3,
             weight_decay=1e-4,
         )
-        embeddings_dict = gnn_embedding.run()
-        embeddings_tensor = embeddings_dict["graph"]
+        gnn_embedding.fit()
+        embeddings_tensor = gnn_embedding.embed()
 
-        embeddings_df = pd.DataFrame(
-            embeddings_tensor.numpy(), index=adjacency_matrix.index
-        )
+        node_names = adjacency_matrix.columns.tolist()
+        embeddings_df = pd.DataFrame(embeddings_tensor.numpy(), index=node_names)
         print("GNN embeddings generated. Shape:", embeddings_df.shape)
 
         graph_embedding = GraphEmbedding(
diff --git a/docs/source/gnns.rst b/docs/source/gnns.rst
index dae6ad5..c73e3e3 100644
--- a/docs/source/gnns.rst
+++ b/docs/source/gnns.rst
@@ -3,10 +3,7 @@
 GNNs for Multi-Omics
 ====================
 
-This section provides an overview of **Graph Neural Networks (GNNs)** in BioNeuralNet
-for multi-omics data. We support four GNN architectures (GCN, GAT, GraphSAGE, GIN), each
-of which can be **task-driven** (using numeric values or other labels per node) or
-**unsupervised** (using intrinsic objectives) to produce node embeddings.
+This section provides an overview of **Graph Neural Networks (GNNs)** in BioNeuralNet for multi-omics data. We support four GNN architectures (GCN, GAT, GraphSAGE, GIN), each of which can be **task-driven** (using numeric values or other labels per node) or **unsupervised** (using intrinsic objectives) to produce node embeddings.
 
 .. contents::
    :local:
@@ -15,8 +12,7 @@ of which can be **task-driven** (using numeric values or other labels per node)
 Graph Convolutional Network (GCN)
 ---------------------------------
 
-GCN layers apply a spectral-based convolution operator to aggregate neighbor
-information. The core update equation for a single GCN layer is:
+GCN layers apply a spectral-based convolution operator to aggregate neighbor information. The core update equation for a single GCN layer is:
 
 .. math::
 
@@ -53,8 +49,7 @@ where
 GraphSAGE
 ---------
 
-GraphSAGE (SAmple and aggreGatE) updates each node by concatenating its own features
-with an aggregated summary of its neighbors. In a mean-aggregator setting:
+GraphSAGE (SAmple and aggreGatE) updates each node by concatenating its own features with an aggregated summary of its neighbors. In a mean-aggregator setting:
 
 .. math::
 
@@ -94,32 +89,81 @@ Task-Driven vs. Unsupervised GNNs
 
 1. **Task-Driven**:
 
-   If each node (e.g., a gene or protein) has a **numeric value**
-   (e.g., correlation with a disease phenotype), you can train a GNN to predict
-   this value with MSE or another loss. This aligns node embeddings with the
-   target measure, grouping nodes that have similar values or relationships.
+   If each node (e.g., a gene or protein) has a **numeric value** (e.g., correlation with a disease phenotype), you can train a GNN to predict this value (with MSE loss). This aligns node embeddings with the target measure, grouping nodes that have similar relationships.
+
+   **Pre-Computed or Random Node Features**:
+
+      - You can correlate each node with clinical variables to build a rich feature vector, **or** simply initialize node features randomly.
+      - Even with random features, the GNN can still learn **the label** drives training via MSE on each node’s predicted value vs. its real label.
 
 2. **Unsupervised**:
+
    If no explicit node label is provided, the GNN can learn from the **graph structure itself**:
 
-   - **Intrinsic Objectives**: Methods like graph autoencoders or contrastive losses let GNNs reconstruct edges, distinguish real vs. random neighbors, etc.
-   - **Structure as Signal**: Even in the absence of external labels, adjacency patterns guide the GNN to produce meaningful embeddings (local/global relationships).
-   - **Practical Implementation**: In some workflows (e.g., a quick exploration), you might skip external labeling entirely. The GNN could run with random weights or minimal self-supervision, still generating a representation that captures the topology to some extent.
+      - **Intrinsic Objectives**:
+
+         e.g., graph autoencoders or contrastive losses.
+
+      - **Random Features**:
+
+         Possibly leaving node features random or minimal.
+
+      - **Structure as Signal**:
+
+         Even in the absence of external labels, adjacency patterns guide the GNN to produce embeddings capturing local/global relationships.
+
+      - **Practical Implementation**:
 
-Overall, both approaches yield node embeddings that can be integrated into **subject-level**
-datasets or used for clustering or further analysis.
+         - In some quick explorations, you might skip external labeling entirely.
+         - The GNN produces a representation that captures the topology.
 
+Overall, both approaches yield node embeddings that can be integrated into **subject-level** datasets or used for clustering or further analysis.
+
+Key Insights into GNN Parameters and Outputs
+--------------------------------------------
+
+1. **Input Parameters**:
+
+   - **Node Features Matrix**: :math:`(N \times F)` shape. You can use pre-computed correlations or random initialization.
+   - **Edge Index**: :math:`(2 \times E)` shape, specifying edges.
+   - **Edge Attributes (Optional)**: Weighted edges, etc.
+   - **Target Labels (Optional)**: If **task-driven** (MSE regression or classification), each node has a numeric or categorical label.
+
+2. **Output Embeddings**:
+
+   - Each node is represented as a dense vector in the hidden layers.
+   - Typically, a final linear may reduce to 1D if you do node-level MSE, but the **penultimate** layer is multi-dimensional.
+   - After training, you retrieve the multi-dimensional “node embeddings” if needed.
+
+3. **Dimensionality Reduction**:
+
+   - After generating embeddings, techniques like PCA can reduce dimensionality for simpler usage and improved interpretability.
+
+How DPMON Uses GNNs Differently
+-------------------------------
+
+**DPMON** (Disease Prediction using Multi-Omics Networks) is an **end-to-end** pipeline
+that uses GNN layers for **sample-level classification**, rather than node-level MSE:
+
+1. **Local + Global Structure**:
+   - DPMON’s GNN still aggregates information per node. However, it doesn’t use numeric per-node labels in MSE.
+   - Instead, it extracts node embeddings to feed into an autoencoder/dimension-averaging pipeline.
+
+2. **Classification Head**:
+   - Ultimately, DPMON’s GNN embeddings are combined with patient-level omics data.
+   - A downstream neural network (e.g., a softmax layer) predicts the patient’s disease phenotype.
+   - This is a **sample-level** classification objective (e.g., CrossEntropyLoss on patient classes).
+
+Hence, DPMON’s approach reuses the **same** GNN architectures (GCN, GAT, etc.) but with a **different final objective** (classification at sample level, instead of node-level MSE).
 
 Example Usage
 -------------
 
-Below is a **simplified** snippet showing a **task-driven** approach, where each node is
-assigned a numeric correlation with disease severity. The GNN (GCN, GAT, SAGE, GIN) tries
-to predict that correlation, producing a **trained** embedding:
+Below is a **simplified** snippet showing a **task-driven** node-level approach, where each node is assigned a numeric correlation with disease severity. The GNN (choose GCN, GAT, SAGE, or GIN) tries to predict that correlation, producing a trained embedding:
 
 .. code-block:: python
 
-   from bioneuralnet.network_embeddings import GNNEmbedding
+   from bioneuralnet.network_embedding import GNNEmbedding
    import pandas as pd
 
    gnn = GNNEmbedding(
@@ -131,27 +175,11 @@ to predict that correlation, producing a **trained** embedding:
        model_type='GAT',
        hidden_dim=64
    )
-   out_dict = gnn.run()
-   node_embeds = out_dict['graph']
-
-
-If **no** numeric label is specified, you can rely on an **unsupervised** approach—either
-a self-supervised pipeline or even a random initialization—for a simpler dimensional
-embedding of the graph structure.
-
-
-How DPMON Uses GNNs Differently
--------------------------------
-
-**DPMON** (Disease Prediction using Multi-Omics Networks) is an **end-to-end** pipeline
-that also employs GNNs, but with a distinct focus:
-
-- **Local + Global Structure**: It captures how nodes relate across the entire multi-omics network without using correlation-based node labels.
-- **Joint Optimization**: The GNN is trained jointly with a downstream neural net for disease phenotype prediction, maximizing predictive power.
-- **Feature Fusion**: The resulting embeddings are integrated into the patient-level multi-omics data, enhancing classification accuracy.
+   gnn.fit()
+   node_embeds = gnn.embed()
 
-By focusing on the **intrinsic connectivity** of the multi-omics graph, DPMON avoids
-overfitting to single-sample signals and achieves more robust predictions.
+If **no** numeric label is used, you can rely on an **unsupervised** or random feature initialization. Even when we skip correlation-based features and MSE training, the GNN still provides embeddings reflecting the graph structure.
 
+For sample-level classification, see :ref:`dpmon`. DPMON integrates node embeddings into an end-to-end disease prediction pipeline, focusing on **patient** labels rather than node-level numeric regression.
 
 Return to :doc:`../index`
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 6ebddb5..150ea24 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -173,6 +173,7 @@ BioNeuralNet offers five core steps in a typical workflow:
    gnns
    user_api
    faq
+   clustering
 
 
 Indices and tables
diff --git a/docs/source/tutorials/example_1.rst b/docs/source/tutorials/example_1.rst
index 5962691..49eb9f4 100644
--- a/docs/source/tutorials/example_1.rst
+++ b/docs/source/tutorials/example_1.rst
@@ -69,7 +69,7 @@ Below is a **complete** snippet:
    adjacency_matrix = smccnet.run()
 
    # 3) Generate embeddings
-   gnn = GNNEmbedding(
+   gnn_embedding = GNNEmbedding(
        adjacency_matrix=adjacency_matrix,
        omics_data=omics_data,
        phenotype_data=phenotype_data,
@@ -83,13 +83,12 @@ Below is a **complete** snippet:
        lr=1e-3,
        weight_decay=1e-4,
    )
-   embedding_dict = gnn.run()
+   gnn_embedding.fit()
+   embeddings_tensor = gnn_embedding.embed()
 
-   # Node embeddings tensor
-   node_embeddings = embedding_dict["graph"]
-
-   node_name = adjacency_matrix.columns.index
-   node_embeddings_df = pd.DataFrame(node_embeddings.numpy(), index=node_name)
+   node_names = adjacency_matrix.columns.tolist()
+   embeddings_df = pd.DataFrame(embeddings_tensor.numpy(), index=node_names)
+   print("GNN embeddings generated. Shape:", embeddings_df.shape)
 
    # 4) Subject-level representation:
    graph_embed = GraphEmbedding(
diff --git a/tests/__init__.py b/tests/__init__.py
index 7202bc0..a83c13c 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -8,3 +8,4 @@
 from .test_feature_selector import *
 from .test_static_visualization import *
 from .test_dynamic_visualization import *
+from .test_dataset_dpmon import *
diff --git a/tests/test_dataset_dpmon.py b/tests/test_dataset_dpmon.py
new file mode 100644
index 0000000..fc7924a
--- /dev/null
+++ b/tests/test_dataset_dpmon.py
@@ -0,0 +1,63 @@
+import unittest
+import pandas as pd
+
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.datasets import DatasetLoader
+from bioneuralnet.downstream_task.dpmon import DPMON
+
+
+class TestDPMONRealData(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        """
+        Load sample dataset and generate adjacency with SmCCNet
+        """
+        cls.datasets = DatasetLoader()
+        cls.omics1, cls.omics2, cls.pheno = cls.datasets("example1")
+
+        cls.omics_list = [cls.omics1, cls.omics2]
+        cls.phenotype_data = cls.pheno.rename(columns={"Pheno": "phenotype"})
+
+        cls.smccnet = SmCCNet(
+            phenotype_df=cls.phenotype_data,
+            omics_dfs=cls.omics_list,
+            data_types=["Genomics", "miRNA"],
+            kfold=5,
+            summarization="PCA",
+            seed=127,
+        )
+        cls.adjacency_matrix = cls.smccnet.run()
+
+    def test_dpmon_run(self):
+        """
+        Test DPMON on real adjacency + real multi-omics data
+        """
+
+        dpmon = DPMON(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_list=self.omics_list,
+            phenotype_data=self.phenotype_data,
+            clinical_data=None,
+            model="GAT",
+            gnn_hidden_dim=16,
+            layer_num=2,
+            nn_hidden_dim1=8,
+            nn_hidden_dim2=8,
+            epoch_num=1,
+            repeat_num=1,
+            lr=0.01,
+            weight_decay=1e-4,
+            gpu=False,
+            tune=False,
+        )
+
+        predictions = dpmon.run()
+
+        self.assertIsInstance(predictions, pd.DataFrame)
+        if not predictions.empty:
+            self.assertIn("Actual", predictions.columns)
+            self.assertIn("Predicted", predictions.columns)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_dataset_gnn_embeddings.py b/tests/test_dataset_gnn_embeddings.py
new file mode 100644
index 0000000..65af4c6
--- /dev/null
+++ b/tests/test_dataset_gnn_embeddings.py
@@ -0,0 +1,71 @@
+import unittest
+import torch
+import pandas as pd
+
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.datasets import DatasetLoader
+from bioneuralnet.network_embedding import GNNEmbedding
+
+
+class TestGNNEmbeddingRealData(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        """
+        Load sample dataset via DatasetLoader and generate adjacency with SmCCNet
+        """
+        cls.datasets = DatasetLoader()
+        cls.omics1, cls.omics2, cls.pheno = cls.datasets("example1")
+
+        cls.smccnet = SmCCNet(
+            phenotype_df=cls.pheno,
+            omics_dfs=[cls.omics1, cls.omics2],
+            data_types=["Genomics", "miRNA"],
+            kfold=5,
+            summarization="PCA",
+            seed=127,
+        )
+        cls.adjacency_matrix = cls.smccnet.run()
+        cls.omics = pd.concat([cls.omics1, cls.omics2], axis=1)
+
+        cls.pheno_data = cls.pheno.rename(columns={"Pheno": "phenotype"})
+
+    def test_gnn_embedding_run(self):
+        """
+        Test GNNEmbedding with real adjacency + multi-omics data to produce node embeddings.
+        """
+        gnn = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics,
+            phenotype_data=self.pheno_data,
+            clinical_data=None,
+            phenotype_col="phenotype",
+            model_type="GAT",
+            hidden_dim=32,
+            layer_num=2,
+            dropout=True,
+            num_epochs=1,
+            lr=1e-3,
+            weight_decay=1e-4,
+            gpu=False,
+        )
+
+        gnn.fit()
+        node_embeddings = gnn.embed()
+
+        self.assertIsInstance(
+            node_embeddings, torch.Tensor, "Embeddings must be a torch.Tensor"
+        )
+        self.assertEqual(
+            node_embeddings.shape[0],
+            self.adjacency_matrix.shape[0],
+            "Number of node embeddings must match adjacency dimension.",
+        )
+        self.assertGreater(
+            node_embeddings.shape[1],
+            1,
+            "Embeddings dimension should be >1 (multi-dimensional).",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_dataset_subject_representation.py b/tests/test_dataset_subject_representation.py
new file mode 100644
index 0000000..c0da2c0
--- /dev/null
+++ b/tests/test_dataset_subject_representation.py
@@ -0,0 +1,59 @@
+import unittest
+import torch
+import pandas as pd
+
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.datasets import DatasetLoader
+from bioneuralnet.subject_representation import GraphEmbedding
+
+
+class TestSubjectRepresentationRealData(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        """
+        Load sample dataset via DatasetLoader and generate adjacency with SmCCNet
+        """
+        cls.datasets = DatasetLoader()
+        cls.omics1, cls.omics2, cls.pheno = cls.datasets("example1")
+        cls.omics = pd.concat([cls.omics1, cls.omics2], axis=1)
+        cls.pheno_data = cls.pheno.rename(columns={"Pheno": "phenotype"})
+
+        cls.smccnet = SmCCNet(
+            phenotype_df=cls.pheno_data,
+            omics_dfs=[cls.omics],
+            data_types=["Genomics+miRNA"],
+            kfold=5,
+            summarization="PCA",
+            seed=127,
+        )
+        cls.adjacency_matrix = cls.smccnet.run()
+
+    def test_graph_embedding_pipeline(self):
+        """
+        Test GraphEmbedding with real adjacency + real omics data (GNN-based approach).
+        """
+        graph_embed = GraphEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics,
+            phenotype_data=self.pheno_data,
+            clinical_data=None,
+            embeddings=None,
+            reduce_method="PCA",
+        )
+
+        enhanced_omics_data = graph_embed.run()
+
+        self.assertIsInstance(enhanced_omics_data, pd.DataFrame)
+        self.assertFalse(
+            enhanced_omics_data.empty, "Enhanced omics data should not be empty."
+        )
+
+        self.assertEqual(
+            enhanced_omics_data.shape[0],
+            self.omics.shape[0],
+            "Enhanced data must have same #samples (rows) as original omics.",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_dpmon.py b/tests/test_dpmon.py
index fbc4730..d87a9f6 100644
--- a/tests/test_dpmon.py
+++ b/tests/test_dpmon.py
@@ -66,17 +66,17 @@ def test_run_with_tune(self, mock_tune):
         mock_tune.assert_called_once()
         self.assertTrue(predictions.empty)
 
-    def test_missing_clinical_data(self):
-        with self.assertRaises(ValueError):
-            DPMON(
-                adjacency_matrix=self.adjacency_matrix,
-                omics_list=[self.omics_data1, self.omics_data2],
-                phenotype_data=self.phenotype_data,
-                clinical_data=None,
-                model="SAGE",
-                tune=False,
-                gpu=False,
-            )
+    def test_empty_clinical_data(self):
+        empty_clinical = pd.DataFrame()
+        DPMON(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_list=[self.omics_data1, self.omics_data2],
+            phenotype_data=self.phenotype_data,
+            clinical_data=empty_clinical,
+            model="SAGE",
+            tune=False,
+            gpu=False,
+        )
 
 
 if __name__ == "__main__":
diff --git a/tests/test_gnn_embedding.py b/tests/test_gnn_embedding.py
index ea18d02..aacd559 100644
--- a/tests/test_gnn_embedding.py
+++ b/tests/test_gnn_embedding.py
@@ -33,13 +33,14 @@ def setUp(self):
 
     @patch.object(
         GNNEmbedding,
-        "_generate_embeddings",
-        return_value=torch.tensor([[0.1, 0.2], [0.3, 0.4]]),
+        "embed",
+        return_value=torch.tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]),
     )
-    def test_run(self, mock_gen_emb):
+    def test_fit_and_embed_with_clinical(self, mock_embed):
         """
-        Ensure GNNEmbedding run() returns 'graph' key in the output dict
-        with an expected torch.Tensor shape.
+        Test the full workflow of GNNEmbedding with clinical data:
+        1. Fit the model.
+        2. Generate embeddings.
         """
         gnn = GNNEmbedding(
             adjacency_matrix=self.adjacency_matrix,
@@ -51,27 +52,305 @@ def test_run(self, mock_gen_emb):
             hidden_dim=2,
             layer_num=2,
             dropout=True,
-            num_epochs=1,
+            num_epochs=10,
             lr=1e-3,
             weight_decay=1e-4,
+            gpu=False,
+            random_feature_dim=16,
+            seed=42,
         )
 
-        embeddings_dict = gnn.run()
-        self.assertIn(
-            "graph",
-            embeddings_dict,
-            "Embeddings dictionary should contain 'graph' key.",
+        gnn.fit()
+        embeddings = gnn.embed()
+
+        mock_embed.assert_called_once()
+        self.assertIsInstance(
+            embeddings, torch.Tensor, "Embeddings should be a torch.Tensor."
+        )
+        self.assertEqual(
+            embeddings.shape, (3, 2), "Embeddings tensor should have shape (3,2)."
+        )
+
+    @patch.object(
+        GNNEmbedding,
+        "embed",
+        return_value=torch.tensor([[0.7, 0.8, 0.9], [1.0, 1.1, 1.2], [1.3, 1.4, 1.5]]),
+    )
+    def test_fit_and_embed_without_clinical(self, mock_embed):
+        """
+        Test the full workflow of GNNEmbedding without clinical data:
+        1. Fit the model.
+        2. Generate embeddings.
+        """
+        gnn = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=None,
+            phenotype_col="phenotype",
+            model_type="GAT",
+            hidden_dim=3,
+            layer_num=3,
+            dropout=False,
+            num_epochs=20,
+            lr=1e-3,
+            weight_decay=1e-4,
+            gpu=False,
+            random_feature_dim=16,
+            seed=123,
+        )
+
+        gnn.fit()
+        embeddings = gnn.embed()
+
+        mock_embed.assert_called_once()
+        self.assertIsInstance(
+            embeddings, torch.Tensor, "Embeddings should be a torch.Tensor."
+        )
+        self.assertEqual(
+            embeddings.shape, (3, 3), "Embeddings tensor should have shape (3,3)."
+        )
+
+    def test_embed_without_fit(self):
+        """
+        Ensure that calling embed() without fit() raises a ValueError.
+        """
+        gnn = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=self.clinical_data,
+            phenotype_col="phenotype",
+            model_type="SAGE",
+            hidden_dim=2,
+            layer_num=2,
+            dropout=True,
+            num_epochs=10,
+            lr=1e-3,
+            weight_decay=1e-4,
+            gpu=False,
+            random_feature_dim=16,
+            seed=42,
         )
 
-        embeddings = embeddings_dict["graph"]
+        with self.assertRaises(ValueError):
+            gnn.embed()
+
+    def test_initialization_with_empty_clinical_data(self):
+        """
+        Test that providing empty clinical_data treats it as None and initializes features randomly.
+        """
+        empty_clinical = pd.DataFrame()
+
+        gnn = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=empty_clinical,
+            phenotype_col="phenotype",
+            model_type="GIN",
+            hidden_dim=2,
+            layer_num=2,
+            dropout=False,
+            num_epochs=10,
+            lr=1e-3,
+            weight_decay=1e-4,
+            gpu=False,
+            random_feature_dim=16,
+            seed=42,
+        )
+
+        with patch.object(
+            gnn,
+            "_generate_embeddings",
+            return_value=torch.tensor([[0.5, 0.6], [0.7, 0.8], [0.9, 1.0]]),
+        ):
+            gnn.fit()
+            embeddings = gnn.embed()
         self.assertIsInstance(
             embeddings, torch.Tensor, "Embeddings should be a torch.Tensor."
         )
         self.assertEqual(
-            embeddings.shape, (2, 2), "Embeddings tensor should have shape (2,2)."
+            embeddings.shape, (3, 2), "Embeddings tensor should have shape (3,2)."
         )
 
-        mock_gen_emb.assert_called_once()
+    def test_empty_adjacency_matrix(self):
+        """
+        Ensure that providing an empty adjacency matrix raises a ValueError.
+        """
+        empty_adj = pd.DataFrame()
+        with self.assertRaises(ValueError):
+            GNNEmbedding(
+                adjacency_matrix=empty_adj,
+                omics_data=self.omics_data,
+                phenotype_data=self.phenotype_data,
+                clinical_data=self.clinical_data,
+                phenotype_col="phenotype",
+                model_type="GCN",
+                hidden_dim=2,
+                layer_num=2,
+                dropout=True,
+                num_epochs=10,
+                lr=1e-3,
+                weight_decay=1e-4,
+                gpu=False,
+                random_feature_dim=16,
+                seed=42,
+            )
+
+    def test_empty_omics_data(self):
+        """
+        Ensure that providing empty omics_data raises a ValueError.
+        """
+        empty_omics = pd.DataFrame()
+        with self.assertRaises(ValueError):
+            GNNEmbedding(
+                adjacency_matrix=self.adjacency_matrix,
+                omics_data=empty_omics,
+                phenotype_data=self.phenotype_data,
+                clinical_data=self.clinical_data,
+                phenotype_col="phenotype",
+                model_type="GCN",
+                hidden_dim=2,
+                layer_num=2,
+                dropout=True,
+                num_epochs=10,
+                lr=1e-3,
+                weight_decay=1e-4,
+                gpu=False,
+                random_feature_dim=16,
+                seed=42,
+            )
+
+    def test_empty_phenotype_data(self):
+        """
+        Ensure that providing empty phenotype_data raises a ValueError.
+        """
+        empty_pheno = pd.DataFrame()
+        with self.assertRaises(ValueError):
+            GNNEmbedding(
+                adjacency_matrix=self.adjacency_matrix,
+                omics_data=self.omics_data,
+                phenotype_data=empty_pheno,
+                clinical_data=self.clinical_data,
+                phenotype_col="phenotype",
+                model_type="GCN",
+                hidden_dim=2,
+                layer_num=2,
+                dropout=True,
+                num_epochs=10,
+                lr=1e-3,
+                weight_decay=1e-4,
+                gpu=False,
+                random_feature_dim=16,
+                seed=42,
+            )
+
+    def test_random_feature_dim(self):
+        """
+        Ensure that random_feature_dim correctly sets the number of features when clinical_data is None.
+        """
+        gnn = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=None,
+            phenotype_col="phenotype",
+            model_type="GCN",
+            hidden_dim=3,
+            layer_num=2,
+            dropout=True,
+            num_epochs=10,
+            lr=1e-3,
+            weight_decay=1e-4,
+            gpu=False,
+            random_feature_dim=8,
+            seed=42,
+        )
+
+        with patch.object(
+            gnn,
+            "_generate_embeddings",
+            return_value=torch.tensor(
+                [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
+            ),
+        ):
+            gnn.fit()
+            embeddings = gnn.embed()
+            print(embeddings)
+
+        self.assertIsInstance(
+            embeddings, torch.Tensor, "Embeddings should be a torch.Tensor."
+        )
+        self.assertEqual(
+            embeddings.shape, (3, 3), "Embeddings tensor should have shape (3,3)."
+        )
+
+    def test_seed_reproducibility(self):
+        """
+        Ensure that setting a seed results in reproducible random features.
+        """
+        gnn1 = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=None,
+            phenotype_col="phenotype",
+            model_type="GCN",
+            hidden_dim=2,
+            layer_num=2,
+            dropout=True,
+            num_epochs=10,
+            lr=1e-3,
+            weight_decay=1e-4,
+            gpu=False,
+            random_feature_dim=4,
+            seed=42,
+        )
+
+        gnn2 = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=None,
+            phenotype_col="phenotype",
+            model_type="GCN",
+            hidden_dim=2,
+            layer_num=2,
+            dropout=True,
+            num_epochs=10,
+            lr=1e-3,
+            weight_decay=1e-4,
+            gpu=False,
+            random_feature_dim=4,
+            seed=42,
+        )
+
+        with patch.object(
+            gnn1,
+            "_generate_embeddings",
+            return_value=torch.tensor(
+                [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]]
+            ),
+        ):
+            gnn1.fit()
+            embeddings1 = gnn1.embed()
+
+        with patch.object(
+            gnn2,
+            "_generate_embeddings",
+            return_value=torch.tensor(
+                [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]]
+            ),
+        ):
+            gnn2.fit()
+            embeddings2 = gnn2.embed()
+
+        # Assertions
+        self.assertTrue(
+            torch.equal(embeddings1, embeddings2),
+            "Embeddings should be identical due to same seed.",
+        )
 
 
 if __name__ == "__main__":
diff --git a/tests/test_pagerank.py b/tests/test_pagerank.py
index 3b3db35..e3060d1 100644
--- a/tests/test_pagerank.py
+++ b/tests/test_pagerank.py
@@ -1,5 +1,5 @@
 import unittest
-from unittest.mock import patch, MagicMock
+from unittest.mock import patch
 import pandas as pd
 import networkx as nx
 from bioneuralnet.clustering import PageRank
@@ -26,14 +26,10 @@ def setUp(self):
 
         self.phenotype_data = pd.Series([1, 0, 1, 0], index=["1", "2", "3", "4"])
 
-    @patch("bioneuralnet.clustering.pagerank.get_logger")
-    def test_generate_weighted_personalization(self, mock_get_logger):
+    def test_generate_weighted_personalization(self):
         """
         Test the generate_weighted_personalization method.
         """
-        mock_logger = MagicMock()
-        mock_get_logger.return_value = mock_logger
-
         pagerank_instance = PageRank(
             graph=self.G,
             omics_data=self.omics_data,
@@ -51,67 +47,54 @@ def test_generate_weighted_personalization(self, mock_get_logger):
         for value in personalization.values():
             self.assertIsInstance(value, float)
 
-    @patch("bioneuralnet.clustering.pagerank.get_logger")
-    def test_run_pagerank_clustering(self, mock_get_logger):
+    @patch.object(
+        PageRank,
+        "generate_weighted_personalization",
+        return_value={"1": 0.5, "2": 0.5},
+    )
+    @patch.object(
+        PageRank,
+        "sweep_cut",
+        return_value=(["1", "2"], 2, 0.1, 0.8, 0.2, "0.8 (0.05)"),
+    )
+    @patch.object(PageRank, "save_results")
+    def test_run_pagerank_clustering(
+        self, mock_save_results, mock_sweep_cut, mock_gen_pers
+    ):
         """
         Test the run_pagerank_clustering method.
         """
-        mock_logger = MagicMock()
-        mock_get_logger.return_value = mock_logger
-
         pagerank_instance = PageRank(
             graph=self.G,
             omics_data=self.omics_data,
             phenotype_data=self.phenotype_data,
             alpha=0.9,
             k=0.9,
+            max_iter=100,
+            tol=1e-6,
             output_dir="test_output",
         )
 
-        with patch.object(
-            pagerank_instance,
-            "generate_weighted_personalization",
-            return_value={"1": 0.5, "2": 0.5},
-        ) as mock_gen_pers, patch.object(
-            pagerank_instance,
-            "sweep_cut",
-            return_value=(["1", "2"], 2, 0.1, 0.8, 0.2, "0.8 (0.05)"),
-        ) as mock_sweep_cut, patch.object(
-            pagerank_instance, "save_results"
-        ) as mock_save_results:
-
-            with patch(
-                "networkx.pagerank",
-                return_value={"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0},
-            ) as mock_pagerank:
-                results = pagerank_instance.run_pagerank_clustering(
-                    seed_nodes=["1", "2"]
-                )
-
-                mock_pagerank.assert_called_once_with(
-                    pagerank_instance.G,
-                    alpha=pagerank_instance.alpha,
-                    personalization={"1": 0.5, "2": 0.5},
-                    max_iter=pagerank_instance.max_iter,
-                    tol=pagerank_instance.tol,
-                    weight="weight",
-                )
-                mock_gen_pers.assert_called_once_with(["1", "2"])
-                mock_sweep_cut.assert_called_once_with(
-                    {"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0}
-                )
-                mock_save_results.assert_called_once_with(
-                    {
-                        "cluster_nodes": ["1", "2"],
-                        "cluster_size": 2,
-                        "conductance": 0.1,
-                        "correlation": 0.8,
-                        "composite_score": 0.2,
-                        "correlation_pvalue": "0.8 (0.05)",
-                    }
-                )
-
-                expected_results = {
+        with patch(
+            "networkx.pagerank",
+            return_value={"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0},
+        ) as mock_pagerank:
+            results = pagerank_instance.run_pagerank_clustering(seed_nodes=["1", "2"])
+
+            mock_pagerank.assert_called_once_with(
+                pagerank_instance.G,
+                alpha=pagerank_instance.alpha,
+                personalization={"1": 0.5, "2": 0.5},
+                max_iter=pagerank_instance.max_iter,
+                tol=pagerank_instance.tol,
+                weight="weight",
+            )
+            mock_gen_pers.assert_called_once_with(["1", "2"])
+            mock_sweep_cut.assert_called_once_with(
+                {"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0}
+            )
+            mock_save_results.assert_called_once_with(
+                {
                     "cluster_nodes": ["1", "2"],
                     "cluster_size": 2,
                     "conductance": 0.1,
@@ -119,24 +102,22 @@ def test_run_pagerank_clustering(self, mock_get_logger):
                     "composite_score": 0.2,
                     "correlation_pvalue": "0.8 (0.05)",
                 }
-                self.assertEqual(results, expected_results)
+            )
 
-                mock_logger.info.assert_any_call(
-                    "Generated personalization vector for seed nodes: ['1', '2']"
-                )
-                mock_logger.info.assert_any_call("PageRank computation completed.")
-                mock_logger.info.assert_any_call(
-                    f"Sweep cut resulted in cluster of size {2} with conductance {0.1} and correlation {0.8}."
-                )
+            expected_results = {
+                "cluster_nodes": ["1", "2"],
+                "cluster_size": 2,
+                "conductance": 0.1,
+                "correlation": 0.8,
+                "composite_score": 0.2,
+                "correlation_pvalue": "0.8 (0.05)",
+            }
+            self.assertEqual(results, expected_results)
 
-    @patch("bioneuralnet.clustering.pagerank.get_logger")
-    def test_run_full_pipeline(self, mock_get_logger):
+    def test_run_full_pipeline(self):
         """
         Test the full run method.
         """
-        mock_logger = MagicMock()
-        mock_get_logger.return_value = mock_logger
-
         pagerank_instance = PageRank(
             graph=self.G,
             omics_data=self.omics_data,
@@ -172,18 +153,10 @@ def test_run_full_pipeline(self, mock_get_logger):
                 },
             )
 
-            mock_logger.info.assert_any_call(
-                "PageRank clustering completed successfully."
-            )
-
-    @patch("bioneuralnet.clustering.pagerank.get_logger")
-    def test_save_results(self, mock_get_logger):
+    def test_save_results(self):
         """
         Test the save_results method.
         """
-        mock_logger = MagicMock()
-        mock_get_logger.return_value = mock_logger
-
         pagerank_instance = PageRank(
             graph=self.G,
             omics_data=self.omics_data,
@@ -201,25 +174,19 @@ def test_save_results(self, mock_get_logger):
             "correlation_pvalue": "0.8 (0.05)",
         }
 
-        with patch.object(pd.DataFrame, "to_csv") as mock_to_csv:
-            with patch("os.path.join", return_value="pagerank_results_test.csv"):
-                pagerank_instance.save_results(results)
+        with patch.object(pd.DataFrame, "to_csv") as mock_to_csv, patch(
+            "os.path.join", return_value="pagerank_results_test.csv"
+        ):
+            pagerank_instance.save_results(results)
 
-                mock_to_csv.assert_called_once_with(
-                    "pagerank_results_test.csv", index=False
-                )
-                mock_logger.info.assert_any_call(
-                    "Clustering results saved to pagerank_results_test.csv"
-                )
+            mock_to_csv.assert_called_once_with(
+                "pagerank_results_test.csv", index=False
+            )
 
-    @patch("bioneuralnet.clustering.pagerank.get_logger")
-    def test_generate_graph_empty(self, mock_get_logger):
+    def test_generate_graph_empty(self):
         """
         Test that running clustering with an empty seed_nodes list raises a ValueError.
         """
-        mock_logger = MagicMock()
-        mock_get_logger.return_value = mock_logger
-
         empty_graph = nx.Graph()
         pagerank_instance = PageRank(
             graph=empty_graph,
@@ -231,71 +198,55 @@ def test_generate_graph_empty(self, mock_get_logger):
             pagerank_instance.run_pagerank_clustering(seed_nodes=[])
 
         self.assertEqual(str(context.exception), "Seed nodes list cannot be empty.")
-        mock_logger.error.assert_called_with(
-            "No seed nodes provided for PageRank clustering."
-        )
 
-    @patch("bioneuralnet.clustering.pagerank.get_logger")
-    def test_run_pagerank_clustering_valid_seed_nodes(self, mock_get_logger):
+    @patch.object(
+        PageRank,
+        "generate_weighted_personalization",
+        return_value={"1": 0.5, "2": 0.5},
+    )
+    @patch.object(
+        PageRank,
+        "sweep_cut",
+        return_value=(["1", "2"], 2, 0.1, 0.8, 0.2, "0.8 (0.05)"),
+    )
+    @patch.object(PageRank, "save_results")
+    def test_run_pagerank_clustering_valid_seed_nodes(
+        self, mock_save_results, mock_sweep_cut, mock_gen_pers
+    ):
         """
         Test run_pagerank_clustering with valid seed nodes.
         """
-        mock_logger = MagicMock()
-        mock_get_logger.return_value = mock_logger
-
         pagerank_instance = PageRank(
             graph=self.G,
             omics_data=self.omics_data,
             phenotype_data=self.phenotype_data,
             alpha=0.9,
             k=0.9,
+            max_iter=100,
+            tol=1e-6,
             output_dir="test_output",
         )
 
-        with patch.object(
-            pagerank_instance,
-            "generate_weighted_personalization",
-            return_value={"1": 0.5, "2": 0.5},
-        ) as mock_gen_pers, patch.object(
-            pagerank_instance,
-            "sweep_cut",
-            return_value=(["1", "2"], 2, 0.1, 0.8, 0.2, "0.8 (0.05)"),
-        ) as mock_sweep_cut, patch.object(
-            pagerank_instance, "save_results"
-        ) as mock_save_results:
-
-            with patch(
-                "networkx.pagerank",
-                return_value={"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0},
-            ) as mock_pagerank:
-                results = pagerank_instance.run_pagerank_clustering(
-                    seed_nodes=["1", "2"]
-                )
-
-                mock_pagerank.assert_called_once_with(
-                    pagerank_instance.G,
-                    alpha=pagerank_instance.alpha,
-                    personalization={"1": 0.5, "2": 0.5},
-                    max_iter=pagerank_instance.max_iter,
-                    tol=pagerank_instance.tol,
-                    weight="weight",
-                )
-                mock_gen_pers.assert_called_once_with(["1", "2"])
-                mock_sweep_cut.assert_called_once_with(
-                    {"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0}
-                )
-                mock_save_results.assert_called_once_with(
-                    {
-                        "cluster_nodes": ["1", "2"],
-                        "cluster_size": 2,
-                        "conductance": 0.1,
-                        "correlation": 0.8,
-                        "composite_score": 0.2,
-                        "correlation_pvalue": "0.8 (0.05)",
-                    }
-                )
-
-                expected_results = {
+        with patch(
+            "networkx.pagerank",
+            return_value={"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0},
+        ) as mock_pagerank:
+            results = pagerank_instance.run_pagerank_clustering(seed_nodes=["1", "2"])
+
+            mock_pagerank.assert_called_once_with(
+                pagerank_instance.G,
+                alpha=pagerank_instance.alpha,
+                personalization={"1": 0.5, "2": 0.5},
+                max_iter=pagerank_instance.max_iter,
+                tol=pagerank_instance.tol,
+                weight="weight",
+            )
+            mock_gen_pers.assert_called_once_with(["1", "2"])
+            mock_sweep_cut.assert_called_once_with(
+                {"1": 0.6, "2": 0.4, "3": 0.0, "4": 0.0}
+            )
+            mock_save_results.assert_called_once_with(
+                {
                     "cluster_nodes": ["1", "2"],
                     "cluster_size": 2,
                     "conductance": 0.1,
@@ -303,15 +254,18 @@ def test_run_pagerank_clustering_valid_seed_nodes(self, mock_get_logger):
                     "composite_score": 0.2,
                     "correlation_pvalue": "0.8 (0.05)",
                 }
-                self.assertEqual(results, expected_results)
+            )
+
+            expected_results = {
+                "cluster_nodes": ["1", "2"],
+                "cluster_size": 2,
+                "conductance": 0.1,
+                "correlation": 0.8,
+                "composite_score": 0.2,
+                "correlation_pvalue": "0.8 (0.05)",
+            }
+            self.assertEqual(results, expected_results)
 
-                mock_logger.info.assert_any_call(
-                    "Generated personalization vector for seed nodes: ['1', '2']"
-                )
-                mock_logger.info.assert_any_call("PageRank computation completed.")
-                mock_logger.info.assert_any_call(
-                    f"Sweep cut resulted in cluster of size {2} with conductance {0.1} and correlation {0.8}."
-                )
 
-    if __name__ == "__main__":
-        unittest.main()
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_smccnet.py b/tests/test_smccnet.py
index 73ef3ee..c846064 100644
--- a/tests/test_smccnet.py
+++ b/tests/test_smccnet.py
@@ -10,13 +10,13 @@ class TestSmCCNet(unittest.TestCase):
     def setUp(self):
         self.phenotype_df = pd.DataFrame(
             {
-                "SampleID": ["S1", "S2", "S3", "S4"],
-                "Phenotype": ["Control", "Treatment", "Control", "Treatment"],
+                "ID": ["S1", "S2", "S3", "S4"],
+                "Phenotype": [0, 1, 0, 1],
             }
         )
         self.omics_df1 = pd.DataFrame(
             {
-                "SampleID": ["S1", "S2", "S3", "S4"],
+                "ID": ["S1", "S2", "S3", "S4"],
                 "GeneA": [1.2, 2.3, 3.1, 4.0],
                 "GeneB": [2.1, 3.4, 1.2, 3.3],
                 "GeneC": [3.3, 1.5, 2.2, 4.1],
@@ -24,23 +24,37 @@ def setUp(self):
         )
         self.omics_df2 = pd.DataFrame(
             {
-                "SampleID": ["S1", "S2", "S3", "S4"],
-                "GeneD": [4.2, 5.3, 6.1, 7.0],
-                "GeneE": [5.1, 6.4, 4.2, 6.3],
-                "GeneF": [6.3, 4.5, 5.2, 7.1],
+                "ID": ["S1", "S2", "S3", "S4"],
+                "ProtD": [4.2, 5.3, 6.1, 7.0],
+                "ProtE": [5.1, 6.4, 4.2, 6.3],
+                "ProtF": [6.3, 4.5, 5.2, 7.1],
             }
         )
 
         self.omics_dfs = [self.omics_df1, self.omics_df2]
-        self.data_types = ["Transcriptomics", "Proteomics"]
+        self.data_types = ["Genomics", "Proteomics"]
 
+    @patch("bioneuralnet.external_tools.smccnet.pd.read_csv")
     @patch("bioneuralnet.external_tools.smccnet.subprocess.run")
-    def test_smccnet_successful_run(self, mock_run):
+    def test_smccnet_successful_run(self, mock_run, mock_read_csv):
         mock_completed_process = MagicMock()
         mock_completed_process.returncode = 0
-        mock_completed_process.stdout = '{"columns":["GeneA","GeneB","GeneC","GeneD","GeneE","GeneF"],"index":["GeneA","GeneB","GeneC","GeneD","GeneE","GeneF"],"data":[[1,0.8,0.3,0.5,0.2,0.1],[0.8,1,0.4,0.6,0.3,0.2],[0.3,0.4,1,0.7,0.4,0.3],[0.5,0.6,0.7,1,0.5,0.4],[0.2,0.3,0.4,0.5,1,0.5],[0.1,0.2,0.3,0.4,0.5,1]]}'
         mock_run.return_value = mock_completed_process
 
+        mock_adjacency = pd.DataFrame(
+            [
+                [1.0, 0.8, 0.5, 0.2, 0.3, 0.4],
+                [0.8, 1.0, 0.6, 0.3, 0.4, 0.5],
+                [0.5, 0.6, 1.0, 0.4, 0.5, 0.6],
+                [0.2, 0.3, 0.4, 1.0, 0.7, 0.4],
+                [0.3, 0.4, 0.5, 0.7, 1.0, 0.5],
+                [0.4, 0.5, 0.6, 0.4, 0.5, 1.0],
+            ],
+            index=["GeneA", "GeneB", "GeneC", "ProtD", "ProtE", "ProtF"],
+            columns=["GeneA", "GeneB", "GeneC", "ProtD", "ProtE", "ProtF"],
+        )
+        mock_read_csv.return_value = mock_adjacency
+
         smccnet = SmCCNet(
             phenotype_df=self.phenotype_df,
             omics_dfs=self.omics_dfs,
@@ -55,21 +69,21 @@ def test_smccnet_successful_run(self, mock_run):
         self.assertEqual(adjacency_matrix.shape, (6, 6))
         self.assertListEqual(
             list(adjacency_matrix.columns),
-            ["GeneA", "GeneB", "GeneC", "GeneD", "GeneE", "GeneF"],
+            ["GeneA", "GeneB", "GeneC", "ProtD", "ProtE", "ProtF"],
         )
         self.assertListEqual(
             list(adjacency_matrix.index),
-            ["GeneA", "GeneB", "GeneC", "GeneD", "GeneE", "GeneF"],
+            ["GeneA", "GeneB", "GeneC", "ProtD", "ProtE", "ProtF"],
         )
         self.assertAlmostEqual(adjacency_matrix.loc["GeneA", "GeneB"], 0.8)
-        self.assertAlmostEqual(adjacency_matrix.loc["GeneD", "GeneF"], 0.4)
+        self.assertAlmostEqual(adjacency_matrix.loc["ProtD", "ProtF"], 0.4)
 
     @patch("bioneuralnet.external_tools.smccnet.subprocess.run")
     def test_smccnet_run_failure(self, mock_run):
         mock_run.side_effect = subprocess.CalledProcessError(
             returncode=1,
             cmd="Rscript SmCCNet.R",
-            stderr='Error in allowWGCNAThreads() : could not find function "allowWGCNAThreads"\nExecution halted',
+            stderr='Some Error"\nExecution halted',
         )
 
         smccnet = SmCCNet(
@@ -118,52 +132,46 @@ def test_no_valid_samples(self, mock_run):
         with self.assertRaises(subprocess.CalledProcessError):
             smccnet.run()
 
-    @patch("bioneuralnet.external_tools.smccnet.subprocess.run")
-    def test_smccnet_single_omics(self, mock_run):
-        """
-        Test SmCCNet with single-omics data. Simulates single-omics mode by
-        verifying the adjacency matrix returned by the R script's JSON output.
-        """
-        mock_completed_process = MagicMock()
-        mock_completed_process.returncode = 0
-        mock_completed_process.stdout = (
-            '{"columns":["GeneA","GeneB","GeneC"],'
-            '"index":["GeneA","GeneB","GeneC"],'
-            '"data":[[1,0.8,0.3],[0.8,1,0.4],[0.3,0.4,1]]}'
-        )
-        mock_run.return_value = mock_completed_process
-
-        single_omics_df = pd.DataFrame(
-            {
-                "SampleID": ["S1", "S2", "S3", "S4"],
-                "GeneA": [1.2, 2.3, 3.1, 4.0],
-                "GeneB": [2.1, 3.4, 1.2, 3.3],
-                "GeneC": [3.3, 1.5, 2.2, 4.1],
-            }
-        )
-
-        smccnet = SmCCNet(
-            phenotype_df=self.phenotype_df,
-            omics_dfs=[single_omics_df],
-            data_types=["Transcriptomics"],
-            kfold=5,
-            summarization="PCA",
-            seed=732,
-        )
-
-        adjacency_matrix = smccnet.run()
-
-        self.assertIsInstance(adjacency_matrix, pd.DataFrame)
-
-        self.assertEqual(adjacency_matrix.shape, (3, 3))
-
-        self.assertListEqual(
-            list(adjacency_matrix.columns), ["GeneA", "GeneB", "GeneC"]
-        )
-        self.assertListEqual(list(adjacency_matrix.index), ["GeneA", "GeneB", "GeneC"])
-
-        self.assertAlmostEqual(adjacency_matrix.loc["GeneA", "GeneB"], 0.8)
-        self.assertAlmostEqual(adjacency_matrix.loc["GeneC", "GeneC"], 1.0)
+    # NOT sure how to effectively test single omics data yet
+
+    # @patch("bioneuralnet.external_tools.smccnet.subprocess.run")
+    # def test_smccnet_single_omics(self, mock_run):
+    #     """
+    #     Test SmCCNet with single-omics data. Simulates single-omics mode by
+    #     verifying the adjacency matrix returned by the R script's JSON output.
+    #     """
+    #     mock_completed_process = MagicMock()
+    #     mock_completed_process.returncode = 0
+    #     mock_run.return_value = mock_completed_process
+
+    #     single_omics_df = pd.DataFrame(
+    #         {
+    #             "ID": ["S1", "S2", "S3", "S4"],
+    #             "GeneA": [1.2, 2.3, 3.1, 4.0],
+    #             "GeneB": [2.1, 3.4, 1.2, 3.3],
+    #             "GeneC": [3.3, 1.5, 2.2, 4.1],
+    #         }
+    #     )
+
+    #     smccnet = SmCCNet(
+    #         phenotype_df=self.phenotype_df,
+    #         omics_dfs=[single_omics_df],
+    #         data_types=["Transcriptomics"],
+    #         kfold=5,
+    #         summarization="PCA",
+    #         seed=732,
+    #     )
+
+    #     adjacency_matrix = smccnet.run()
+
+    #     self.assertIsInstance(adjacency_matrix, pd.DataFrame)
+
+    #     self.assertEqual(adjacency_matrix.shape, (3, 3))
+
+    #     self.assertListEqual(
+    #         list(adjacency_matrix.columns), ["GeneA", "GeneB", "GeneC"]
+    #     )
+    #     self.assertListEqual(list(adjacency_matrix.index), ["GeneA", "GeneB", "GeneC"])
 
 
 if __name__ == "__main__":
diff --git a/tests/test_subject_representation.py b/tests/test_subject_representation.py
index 0425885..3287d90 100644
--- a/tests/test_subject_representation.py
+++ b/tests/test_subject_representation.py
@@ -19,17 +19,16 @@ def setUp(self):
             index=["sample1", "sample2", "sample3"],
         )
 
-        self.omics_data["finalgold_visit"] = [0, 1, 2]
-
         self.clinical_data_df = pd.DataFrame(
-            {"age": [30, 40, 50]}, index=["sample1", "sample2", "sample3"]
+            {"age": [30, 40, 50], "bmi": [22.5, 25.0, 28.0]},
+            index=["sample1", "sample2", "sample3"],
         )
 
         self.phenotype_data = pd.Series(
             [0, 1, 2], index=["sample1", "sample2", "sample3"]
         )
 
-        self.embeddings = pd.DataFrame(
+        self.precomputed_embeddings = pd.DataFrame(
             {
                 "dim1": [0.1, 0.2, 0.3],
                 "dim2": [0.4, 0.5, 0.6],
@@ -42,31 +41,33 @@ def setUp(self):
         GraphEmbedding,
         "generate_embeddings",
         return_value=pd.DataFrame(
-            {"dim1": [0.1, 0.2, 0.3]}, index=["gene1", "gene2", "gene3"]
+            {"dim1": [0.1, 0.2, 0.3], "dim2": [0.4, 0.5, 0.6], "dim3": [0.7, 0.8, 0.9]},
+            index=["gene1", "gene2", "gene3"],
         ),
     )
     @patch.object(
         GraphEmbedding,
         "reduce_embeddings",
-        return_value=pd.Series({"gene1": 0.1, "gene2": 0.2, "gene3": 0.3}),
+        return_value=pd.Series({"gene1": 0.5, "gene2": 0.6, "gene3": 0.7}),
     )
     @patch.object(
         GraphEmbedding,
         "integrate_embeddings",
         return_value=pd.DataFrame(
             {
-                "gene1": [1.1, 2.2, 3.3],
-                "gene2": [4.4, 5.5, 6.6],
-                "gene3": [7.7, 8.8, 9.9],
-                "finalgold_visit": [0, 1, 2],
+                "gene1": [1.0 * 0.5, 2.0 * 0.5, 3.0 * 0.5],
+                "gene2": [4.0 * 0.6, 5.0 * 0.6, 6.0 * 0.6],
+                "gene3": [7.0 * 0.7, 8.0 * 0.7, 9.0 * 0.7],
             },
             index=["sample1", "sample2", "sample3"],
         ),
     )
-    def test_run(self, mock_integrate, mock_reduce, mock_generate):
+    def test_run_with_clinical_and_no_precomputed_embeddings(
+        self, mock_integrate, mock_reduce, mock_generate
+    ):
         """
         Test that GraphEmbedding.run() returns an expected DataFrame
-        and calls underlying steps.
+        and calls underlying steps when clinical data is provided and no precomputed embeddings.
         """
         graph_embed = GraphEmbedding(
             adjacency_matrix=self.adjacency_matrix,
@@ -74,13 +75,76 @@ def test_run(self, mock_integrate, mock_reduce, mock_generate):
             phenotype_data=self.phenotype_data,
             clinical_data=self.clinical_data_df,
             embeddings=None,
+            reduce_method="PCA",
         )
 
         enhanced_omics_data = graph_embed.run()
 
         self.assertIsInstance(
-            enhanced_omics_data,
-            pd.DataFrame,
+            enhanced_omics_data, pd.DataFrame, "Output should be a pandas DataFrame."
+        )
+        self.assertEqual(
+            enhanced_omics_data.shape,
+            (3, 3),
+            "Output shape should match expected shape (3,3).",
+        )
+        self.assertListEqual(
+            list(enhanced_omics_data.columns),
+            ["gene1", "gene2", "gene3"],
+            "Columns should match the integrated omics features.",
+        )
+
+        # Ensure that the methods were called once
+        mock_generate.assert_called_once()
+        mock_reduce.assert_called_once_with(mock_generate.return_value)
+        mock_integrate.assert_called_once_with(mock_reduce.return_value)
+
+    @patch.object(
+        GraphEmbedding,
+        "generate_embeddings",
+        return_value=pd.DataFrame(
+            {"dim1": [0.1, 0.2, 0.3], "dim2": [0.4, 0.5, 0.6], "dim3": [0.7, 0.8, 0.9]},
+            index=["gene1", "gene2", "gene3"],
+        ),
+    )
+    @patch.object(
+        GraphEmbedding,
+        "reduce_embeddings",
+        return_value=pd.Series({"gene1": 0.5, "gene2": 0.6, "gene3": 0.7}),
+    )
+    @patch.object(
+        GraphEmbedding,
+        "integrate_embeddings",
+        return_value=pd.DataFrame(
+            {
+                "gene1": [1.0 * 0.5, 2.0 * 0.5, 3.0 * 0.5],
+                "gene2": [4.0 * 0.6, 5.0 * 0.6, 6.0 * 0.6],
+                "gene3": [7.0 * 0.7, 8.0 * 0.7, 9.0 * 0.7],
+                "finalgold_visit": [0, 1, 2],
+            },
+            index=["sample1", "sample2", "sample3"],
+        ),
+    )
+    def test_run_with_precomputed_embeddings(
+        self, mock_integrate, mock_reduce, mock_generate
+    ):
+        """
+        Test that GraphEmbedding.run() returns an expected DataFrame
+        and calls underlying steps when precomputed embeddings are provided.
+        """
+        graph_embed = GraphEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=self.clinical_data_df,
+            embeddings=self.precomputed_embeddings,
+            reduce_method="AVG",
+        )
+
+        enhanced_omics_data = graph_embed.run()
+
+        self.assertIsInstance(
+            enhanced_omics_data, pd.DataFrame, "Output should be a pandas DataFrame."
         )
         self.assertEqual(
             enhanced_omics_data.shape,
@@ -90,17 +154,17 @@ def test_run(self, mock_integrate, mock_reduce, mock_generate):
         self.assertListEqual(
             list(enhanced_omics_data.columns),
             ["gene1", "gene2", "gene3", "finalgold_visit"],
-            "Columns should match the integrated omics + finalgold_visit.",
+            "Columns should match the integrated omics features.",
         )
 
         mock_generate.assert_called_once()
-        mock_reduce.assert_called_once()
-        mock_integrate.assert_called_once()
+        mock_reduce.assert_called_once_with(mock_generate.return_value)
+        mock_integrate.assert_called_once_with(mock_reduce.return_value)
 
     @patch.object(GraphEmbedding, "reduce_embeddings")
-    def test_reduce_embeddings_average(self, mock_reduce):
+    def test_reduce_embeddings_avg(self, mock_reduce):
         """
-        Test that reduce_embeddings works with the 'average' method.
+        Test that reduce_embeddings works with the 'AVG' method.
         """
         mock_reduce.return_value = pd.Series({"gene1": 0.4, "gene2": 0.5, "gene3": 0.6})
         graph_embed = GraphEmbedding(
@@ -109,19 +173,24 @@ def test_reduce_embeddings_average(self, mock_reduce):
             phenotype_data=self.phenotype_data,
             clinical_data=self.clinical_data_df,
             embeddings=None,
+            reduce_method="AVG",
+        )
+        embeddings_df = pd.DataFrame(
+            {"dim1": [0.1, 0.2, 0.3], "dim2": [0.4, 0.5, 0.6], "dim3": [0.7, 0.8, 0.9]},
+            index=["gene1", "gene2", "gene3"],
         )
-        result = graph_embed.reduce_embeddings(self.embeddings, method="average")
+        result = graph_embed.reduce_embeddings(embeddings_df)
 
         self.assertIsInstance(result, pd.Series)
         self.assertListEqual(
-            result.tolist(), [0.4, 0.5, 0.6], "Result should match mocked values."
+            list(result), [0.4, 0.5, 0.6], "Result should match mocked values."
         )
-        mock_reduce.assert_called_once_with(self.embeddings, method="average")
+        mock_reduce.assert_called_once_with(embeddings_df)
 
     @patch.object(GraphEmbedding, "reduce_embeddings")
-    def test_reduce_embeddings_maximum(self, mock_reduce):
+    def test_reduce_embeddings_max(self, mock_reduce):
         """
-        Test that reduce_embeddings works with the 'maximum' method.
+        Test that reduce_embeddings works with the 'MAX' method.
         """
         mock_reduce.return_value = pd.Series({"gene1": 0.7, "gene2": 0.8, "gene3": 0.9})
         graph_embed = GraphEmbedding(
@@ -130,14 +199,55 @@ def test_reduce_embeddings_maximum(self, mock_reduce):
             phenotype_data=self.phenotype_data,
             clinical_data=self.clinical_data_df,
             embeddings=None,
+            reduce_method="MAX",
         )
-        result = graph_embed.reduce_embeddings(self.embeddings, method="maximum")
+        embeddings_df = pd.DataFrame(
+            {"dim1": [0.1, 0.2, 0.3], "dim2": [0.4, 0.5, 0.6], "dim3": [0.7, 0.8, 0.9]},
+            index=["gene1", "gene2", "gene3"],
+        )
+        result = graph_embed.reduce_embeddings(embeddings_df)
 
         self.assertIsInstance(result, pd.Series)
         self.assertListEqual(
-            result.tolist(), [0.7, 0.8, 0.9], "Result should match mocked values."
+            list(result), [0.7, 0.8, 0.9], "Result should match mocked values."
+        )
+        mock_reduce.assert_called_once_with(embeddings_df)
+
+    @patch.object(GraphEmbedding, "integrate_embeddings")
+    def test_integrate_embeddings(self, mock_integrate):
+        """
+        Test that integrate_embeddings correctly integrates the reduced embeddings.
+        """
+        node_embedding_values = pd.Series({"gene1": 0.5, "gene2": 0.6, "gene3": 0.7})
+        expected_output = pd.DataFrame(
+            {
+                "gene1": [1.0 * 0.5, 2.0 * 0.5, 3.0 * 0.5],
+                "gene2": [4.0 * 0.6, 5.0 * 0.6, 6.0 * 0.6],
+                "gene3": [7.0 * 0.7, 8.0 * 0.7, 9.0 * 0.7],
+            },
+            index=["sample1", "sample2", "sample3"],
+        )
+        mock_integrate.return_value = expected_output
+
+        graph_embed = GraphEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=self.clinical_data_df,
+            embeddings=None,
+            reduce_method="PCA",
+        )
+
+        result = graph_embed.integrate_embeddings(node_embedding_values)
+
+        self.assertIsInstance(result, pd.DataFrame)
+        self.assertEqual(result.shape, (3, 3))
+        self.assertListEqual(
+            list(result.columns),
+            ["gene1", "gene2", "gene3"],
+            "Columns should match the integrated omics features.",
         )
-        mock_reduce.assert_called_once_with(self.embeddings, method="maximum")
+        mock_integrate.assert_called_once_with(node_embedding_values)
 
 
 if __name__ == "__main__":

From 638aca6b51d9273c562a11576e0debbe17d9d738 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 27 Jan 2025 09:15:06 -0700
Subject: [PATCH 16/26] Update pre-commit and path using pathlib

---
 .github/workflows/pre-commit.yml       | 14 ++++++++++++++
 bioneuralnet/external_tools/smccnet.py | 10 ++++++++--
 2 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index 58901c2..d8b0fcb 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -37,5 +37,19 @@ jobs:
           pip install torch
           pip install torch_geometric
 
+      - name: Install R
+        uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: "latest"
+
+      - name: Install R packages
+        run: |
+          Rscript -e "if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager', repos='https://cran.r-project.org')"
+          Rscript -e "install.packages(c('dplyr', 'jsonlite'), repos='https://cran.r-project.org')"
+          Rscript -e "BiocManager::install(c('impute', 'preprocessCore', 'GO.db', 'AnnotationDbi'), update=FALSE, ask=FALSE)"
+          Rscript -e "install.packages('SmCCNet', repos='https://cran.r-project.org')"
+          Rscript -e "install.packages('WGCNA', repos='https://cran.r-project.org')"
+        shell: bash
+
       - name: Run Pre-Commit Checks
         run: pre-commit run --all-files --show-diff-on-failure
diff --git a/bioneuralnet/external_tools/smccnet.py b/bioneuralnet/external_tools/smccnet.py
index ad154b8..dee7b6d 100644
--- a/bioneuralnet/external_tools/smccnet.py
+++ b/bioneuralnet/external_tools/smccnet.py
@@ -4,6 +4,7 @@
 from typing import List, Dict, Any
 from ..utils.logger import get_logger
 import json
+from pathlib import Path
 
 
 class SmCCNet:
@@ -197,9 +198,14 @@ def run(self) -> pd.DataFrame:
             self.run_smccnet(serialized_data)
 
             self.logger.info("Loading adjacency matrix from CSV.")
-            adjacency_df = pd.read_csv(
-                f"{os.getcwd()}\\adjacencyMatrix.csv", index_col=0
+            adjacency_path = Path.cwd() / "adjacencyMatrix.csv"
+            self.logger.debug(f"Adjacency matrix path: {adjacency_path}")
+
+            adjacency_df = pd.read_csv(adjacency_path, index_col=0)
+            self.logger.info(
+                "Adjacency matrix loaded with shape: %s", adjacency_df.shape
             )
+
             self.logger.info(
                 "Adjacency matrix loaded with shape: %s", adjacency_df.shape
             )

From 98fd6d51eac99f8b7319fdeaa49846dcc277fd7b Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 27 Jan 2025 09:44:08 -0700
Subject: [PATCH 17/26] Update pre-commit, using mac-os for fast R install

---
 .github/workflows/pre-commit.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index d8b0fcb..6812e8c 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -10,7 +10,7 @@ on:
 
 jobs:
   pre-commit:
-    runs-on: ubuntu-latest
+    runs-on: macos-latest
 
     steps:
       - name: Checkout code
@@ -45,10 +45,8 @@ jobs:
       - name: Install R packages
         run: |
           Rscript -e "if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager', repos='https://cran.r-project.org')"
-          Rscript -e "install.packages(c('dplyr', 'jsonlite'), repos='https://cran.r-project.org')"
           Rscript -e "BiocManager::install(c('impute', 'preprocessCore', 'GO.db', 'AnnotationDbi'), update=FALSE, ask=FALSE)"
-          Rscript -e "install.packages('SmCCNet', repos='https://cran.r-project.org')"
-          Rscript -e "install.packages('WGCNA', repos='https://cran.r-project.org')"
+          Rscript -e "BiocManager::install(c('SmCCNet', 'WGCNA'), update=FALSE, ask=FALSE)"
         shell: bash
 
       - name: Run Pre-Commit Checks

From a24fbf476f383c3f89523b67f46011c6db72f186 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 27 Jan 2025 09:58:51 -0700
Subject: [PATCH 18/26] Update pre-commit for macos brew clean up

---
 .github/workflows/pre-commit.yml | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index 6812e8c..0a456b9 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -28,14 +28,11 @@ jobs:
           key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt', '**/requirements-dev.txt') }}
           restore-keys: ${{ runner.os }}-pip-
 
-      - name: Install Python dependencies
+      - name: Install system dependencies
         run: |
-          set -e
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt
-          pip install -r requirements-dev.txt
-          pip install torch
-          pip install torch_geometric
+          brew update
+          brew install libxml2 qpdf pkgconfig checkbashisms ghostscript
+          brew cleanup
 
       - name: Install R
         uses: r-lib/actions/setup-r@v2
@@ -45,9 +42,9 @@ jobs:
       - name: Install R packages
         run: |
           Rscript -e "if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager', repos='https://cran.r-project.org')"
+          Rscript -e "install.packages('dplyr', repos='https://cran.r-project.org')"
           Rscript -e "BiocManager::install(c('impute', 'preprocessCore', 'GO.db', 'AnnotationDbi'), update=FALSE, ask=FALSE)"
-          Rscript -e "BiocManager::install(c('SmCCNet', 'WGCNA'), update=FALSE, ask=FALSE)"
-        shell: bash
+          Rscript -e "install.packages(c('SmCCNet', 'WGCNA'), repos='https://cran.r-project.org')"
 
       - name: Run Pre-Commit Checks
         run: pre-commit run --all-files --show-diff-on-failure

From b68ef4bd9d7196abebf66ef0efeb4c95445e00fa Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 27 Jan 2025 10:12:25 -0700
Subject: [PATCH 19/26] Update pre-commit to check for python dependencies

---
 .github/workflows/pre-commit.yml | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index 0a456b9..ad84043 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -28,10 +28,19 @@ jobs:
           key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt', '**/requirements-dev.txt') }}
           restore-keys: ${{ runner.os }}-pip-
 
+      - name: Install Python dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install -r requirements-dev.txt
+
+      - name: Verify pre-commit installation
+        run: pre-commit --version
+
       - name: Install system dependencies
         run: |
           brew update
-          brew install libxml2 qpdf pkgconfig checkbashisms ghostscript
+          brew install qpdf pkgconf ghostscript
           brew cleanup
 
       - name: Install R
@@ -40,6 +49,8 @@ jobs:
           r-version: "latest"
 
       - name: Install R packages
+        env:
+          R_LIBS_USER: ${{ runner.temp }}/Library
         run: |
           Rscript -e "if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager', repos='https://cran.r-project.org')"
           Rscript -e "install.packages('dplyr', repos='https://cran.r-project.org')"

From f360b70e1d6a3607bd67f6887f4f631f9e13d8ce Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 27 Jan 2025 10:23:42 -0700
Subject: [PATCH 20/26] Update pre-commit #2

---
 .github/workflows/pre-commit.yml | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index ad84043..a3b8ad7 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -40,8 +40,7 @@ jobs:
       - name: Install system dependencies
         run: |
           brew update
-          brew install qpdf pkgconf ghostscript
-          brew cleanup
+          brew upgrade
 
       - name: Install R
         uses: r-lib/actions/setup-r@v2
@@ -49,13 +48,13 @@ jobs:
           r-version: "latest"
 
       - name: Install R packages
-        env:
-          R_LIBS_USER: ${{ runner.temp }}/Library
         run: |
           Rscript -e "if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager', repos='https://cran.r-project.org')"
-          Rscript -e "install.packages('dplyr', repos='https://cran.r-project.org')"
+          Rscript -e "install.packages(c('dplyr', 'jsonlite'), repos='https://cran.r-project.org')"
           Rscript -e "BiocManager::install(c('impute', 'preprocessCore', 'GO.db', 'AnnotationDbi'), update=FALSE, ask=FALSE)"
-          Rscript -e "install.packages(c('SmCCNet', 'WGCNA'), repos='https://cran.r-project.org')"
+          Rscript -e "install.packages('SmCCNet', repos='https://cran.r-project.org')"
+          Rscript -e "install.packages('WGCNA', repos='https://cran.r-project.org')"
+        shell: bash
 
       - name: Run Pre-Commit Checks
         run: pre-commit run --all-files --show-diff-on-failure

From d0261283ceaabc1264b19d0e03426c8f1f2cf3e3 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Mon, 27 Jan 2025 10:33:01 -0700
Subject: [PATCH 21/26] Update pre-commit #3

---
 .github/workflows/pre-commit.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index a3b8ad7..eadad86 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -33,6 +33,9 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements.txt
           pip install -r requirements-dev.txt
+          pip install torch
+          pip install torch_geometric
+        shell: bash
 
       - name: Verify pre-commit installation
         run: pre-commit --version

From d9db29d3fb031378338fc91f18420a86d5f2a961 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 6 Feb 2025 07:26:52 -0700
Subject: [PATCH 22/26] Hyper parameter tunning added to subject represention
 and gnn embeddings process

---
 .../network_embedding/gnn_embedding.py        | 253 +++++++-----
 .../subject_representation.py                 | 377 +++++++++++-------
 2 files changed, 395 insertions(+), 235 deletions(-)

diff --git a/bioneuralnet/network_embedding/gnn_embedding.py b/bioneuralnet/network_embedding/gnn_embedding.py
index 963b0ce..d4c65c3 100644
--- a/bioneuralnet/network_embedding/gnn_embedding.py
+++ b/bioneuralnet/network_embedding/gnn_embedding.py
@@ -10,6 +10,17 @@
 from .gnn_models import GCN, GAT, SAGE, GIN
 from ..utils.logger import get_logger
 
+from ray import tune
+from ray.tune import CLIReporter
+from ray.air import session
+from ray.tune.schedulers import ASHAScheduler
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+import os
+
 
 class GNNEmbedding:
     """
@@ -33,26 +44,27 @@ def __init__(
         gpu: bool = False,
         random_feature_dim: int = 16,
         seed: Optional[int] = None,
+        tune: Optional[bool] = False,
     ):
         """
         Initializes the GNNEmbedding instance.
 
-        Parameters
-        adjacency_matrix : pd.DataFrame
-        omics_data : pd.DataFrame
-        phenotype_data : pd.DataFrame
-        clinical_data : Optional[pd.DataFrame], default=None
-        phenotype_col : str, optional
-        model_type : str, optional
-        hidden_dim : int, optional
-        layer_num : int, optional
-        dropout : bool, optional
-        num_epochs : int, optional
-        lr : float, optional
-        weight_decay : float, optional
-        gpu : bool, optional
-        random_feature_dim : int, optional
-        seed : Optional[int], default=None
+        Parameters:
+            adjacency_matrix : pd.DataFrame
+            omics_data : pd.DataFrame
+            phenotype_data : pd.DataFrame
+            clinical_data : Optional[pd.DataFrame], default=None
+            phenotype_col : str, optional
+            model_type : str, optional
+            hidden_dim : int, optional
+            layer_num : int, optional
+            dropout : bool, optional
+            num_epochs : int, optional
+            lr : float, optional
+            weight_decay : float, optional
+            gpu : bool, optional
+            random_feature_dim : int, optional
+            seed : Optional[int], default=None
         """
         self.logger = get_logger(__name__)
         if seed is not None:
@@ -67,10 +79,11 @@ def __init__(
         if phenotype_data.empty or phenotype_col not in phenotype_data.columns:
             raise ValueError(f"Phenotype data must have column '{phenotype_col}'.")
         if clinical_data is not None and clinical_data.empty:
-            self.logger.warning(
-                "Clinical data provided is empty ... using random features."
-            )
-            clinical_data = None
+            raise ValueError("Clinical data cannot be empty.")
+            # self.logger.warning(
+            #     "Clinical data provided is empty ... using random features."
+            # )
+            # clinical_data = None
 
         self.adjacency_matrix = adjacency_matrix
         self.omics_data = omics_data
@@ -90,13 +103,12 @@ def __init__(
         self.device = torch.device(
             "cuda" if gpu and torch.cuda.is_available() else "cpu"
         )
-        self.logger.info(
-            f"Initialized GNNEmbedding for regression. device={self.device}"
-        )
+        self.logger.info(f"Initialized GNNEmbedding. device={self.device}")
 
         self.model = None
         self.data = None
         self.embeddings = None
+        self.tune = tune
 
     def fit(self) -> None:
         """
@@ -125,6 +137,13 @@ def embed(self) -> torch.Tensor:
             ValueError
         """
         self.logger.info("Generating node embeddings.")
+        if self.tune:
+            self.logger.info(
+                "Tuning is enabled. Running hyperparameter tuning for GNNEmbedding."
+            )
+            best_config = self.run_gnn_embedding_tuning(num_samples=5)
+            self.logger.info(f"Best tuning config: {best_config}")
+
         if self.model is None or self.data is None:
             self.logger.error(
                 "Model has not been trained. Call 'fit()' before 'embed()'."
@@ -132,7 +151,6 @@ def embed(self) -> torch.Tensor:
             raise ValueError(
                 "Model has not been trained. Call 'fit()' before 'embed()'."
             )
-
         try:
             self.embeddings = self._generate_embeddings(self.model, self.data)
             self.logger.info("Node embeddings generated successfully.")
@@ -143,63 +161,53 @@ def embed(self) -> torch.Tensor:
 
     def _prepare_node_features(self) -> pd.DataFrame:
         """
-        Build node features by correlating each omics feature with each clinical variable
-        (if clinical data is provided). Otherwise, initialize node features randomly.
+        Build node feature vector by computing, for each omics feature (node) in the network,
+        the Pearson correlation (across patients) between the features values (from omics_data)
+        and each clinical variable (from clinical_data).
+
+        The network (adjacency matrix) is assumed to capture relationships between omics features,
+        so its nodes should match the columns of the omics_data. Patient-level data (rows) in
+        omics_data, phenotype_data, and clinical_data are assumed to be aligned.
 
         Returns:
-            pd.DataFrame
+            pd.DataFrame: A node feature matrix where rows are features (nodes) and columns are clinical variables.
         """
         self.logger.info("Preparing node features.")
-        node_names = self.adjacency_matrix.index.tolist()
 
-        if self.clinical_data is not None:
-            self.logger.info(
-                "Clinical data provided. Preparing node features based on correlations."
+        network_features = set(self.adjacency_matrix.index)
+        omics_features = set(self.omics_data.columns)
+        common_features = list(network_features.intersection(omics_features))
+        if len(common_features) == 0:
+            raise ValueError(
+                "No common features found between the network and omics data."
             )
-            common_samples = self.omics_data.index.intersection(
-                self.phenotype_data.index
-            ).intersection(self.clinical_data.index)
-            if len(common_samples) == 0:
-                raise ValueError(
-                    "No common samples among omics, phenotype, and clinical data."
-                )
+        self.logger.info(
+            f"Found {len(common_features)} common features between network and omics data."
+        )
 
-            omics_filtered = self.omics_data.loc[common_samples]
-            clinical_filtered = self.clinical_data.loc[common_samples]
-            clinical_cols = clinical_filtered.columns.tolist()
-
-            node_features_list = []
-            for node in node_names:
-                if node not in omics_filtered.columns:
-                    raise ValueError(f"Node '{node}' not found in omics_data.")
-                corr_vector = []
-                for cvar in clinical_cols:
-                    corr_val = omics_filtered[node].corr(clinical_filtered[cvar])
-                    corr_vector.append(corr_val if not pd.isna(corr_val) else 0.0)
-                node_features_list.append(corr_vector)
-
-            node_features_df = pd.DataFrame(
-                node_features_list, index=node_names, columns=clinical_cols
-            ).fillna(0.0)
-            self.logger.info("Node features prepared based on clinical data.")
-            return node_features_df
+        self.adjacency_matrix = self.adjacency_matrix.loc[
+            common_features, common_features
+        ]
 
-        else:
-            self.logger.info(
-                "No clinical data provided. Initializing node features randomly."
-            )
-            num_nodes = len(node_names)
-            num_features = self.random_feature_dim
-            random_features = np.random.rand(num_nodes, num_features)
-            node_features_df = pd.DataFrame(
-                random_features,
-                index=node_names,
-                columns=[f"RandomFeat_{i+1}" for i in range(num_features)],
-            )
-            self.logger.info(
-                f"Random node features initialized with shape {node_features_df.shape}."
-            )
-            return node_features_df
+        clinical_cols = (
+            list(self.clinical_data.columns) if self.clinical_data is not None else []
+        )
+
+        node_features_list = []
+        for node in common_features:
+            if node not in self.omics_data.columns:
+                raise ValueError(f"Feature '{node}' not found in omics_data columns.")
+            corr_vector = []
+            for cvar in clinical_cols:
+                corr_val = self.omics_data[node].corr(self.clinical_data[cvar])
+                corr_vector.append(corr_val if not pd.isna(corr_val) else 0.0)
+            node_features_list.append(corr_vector)
+
+        node_features_df = pd.DataFrame(
+            node_features_list, index=common_features, columns=clinical_cols
+        ).fillna(0.0)
+        self.logger.info("Node features prepared based on clinical data.")
+        return node_features_df
 
     def _prepare_node_labels(self) -> pd.Series:
         """
@@ -212,12 +220,12 @@ def _prepare_node_labels(self) -> pd.Series:
             f"Preparing node labels by correlating each omics feature with phenotype column '{self.phenotype_col}'."
         )
         common_samples = self.omics_data.index.intersection(self.phenotype_data.index)
+        print(common_samples)
+
         if len(common_samples) == 0:
             raise ValueError("No common samples between omics data and phenotype data.")
-
         omics_filtered = self.omics_data.loc[common_samples]
         phen_filtered = self.phenotype_data.loc[common_samples, self.phenotype_col]
-
         labels_dict = {}
         node_names = self.adjacency_matrix.index.tolist()
         for node in node_names:
@@ -225,7 +233,6 @@ def _prepare_node_labels(self) -> pd.Series:
                 raise ValueError(f"Node '{node}' not found in omics_data columns.")
             corr_val = omics_filtered[node].corr(phen_filtered)
             labels_dict[node] = corr_val if not pd.isna(corr_val) else 0.0
-
         labels_series = pd.Series(labels_dict, index=node_names).fillna(0.0)
         self.logger.info("Node labels prepared successfully.")
         return labels_series
@@ -235,10 +242,9 @@ def _build_pyg_data(
     ) -> Data:
         """
         Construct a PyTorch Geometric Data object:
-
-        - data.x = node_features
-        - data.y = node_labels
-        - data.edge_index from adjacency
+          - data.x = node_features
+          - data.y = node_labels
+          - data.edge_index from adjacency
 
         Returns:
             PyG Data object with x, y, edge_index.
@@ -247,7 +253,6 @@ def _build_pyg_data(
         G = nx.from_pandas_adjacency(self.adjacency_matrix)
         node_mapping = {name: i for i, name in enumerate(node_features.index)}
         G = nx.relabel_nodes(G, node_mapping)
-
         data = from_networkx(G)
         node_order = list(node_features.index)
         data.x = torch.tensor(node_features.loc[node_order].values, dtype=torch.float)
@@ -261,17 +266,13 @@ def _initialize_gnn_model(self) -> nn.Module:
 
         Returns:
             nn.Module
-
         """
         self.logger.info(
             f"Initializing GNN model of type '{self.model_type}' with hidden_dim={self.hidden_dim} and layer_num={self.layer_num}."
         )
-
         if self.data is None or not hasattr(self.data, "x") or self.data.x is None:
             raise ValueError("Data is not initialized or is missing the 'x' attribute.")
-
         input_dim = self.data.x.shape[1]
-
         if self.model_type.upper() == "GCN":
             return GCN(
                 input_dim=input_dim,
@@ -312,14 +313,10 @@ def _train_gnn(self, model: nn.Module, data: Data) -> None:
         data = data.to(self.device)
         model.to(self.device)
         model.train()
-
         criterion = nn.MSELoss()
         optimizer = optim.Adam(
-            model.parameters(),
-            lr=self.lr,
-            weight_decay=self.weight_decay,
+            model.parameters(), lr=self.lr, weight_decay=self.weight_decay
         )
-
         for epoch in range(1, self.num_epochs + 1):
             optimizer.zero_grad()
             out = model(data)
@@ -327,12 +324,10 @@ def _train_gnn(self, model: nn.Module, data: Data) -> None:
             loss = criterion(out, data.y)
             loss.backward()
             optimizer.step()
-
             if epoch % 10 == 0 or epoch == 1:
                 self.logger.info(
                     f"Epoch [{epoch}/{self.num_epochs}], MSE Loss: {loss.item():.4f}"
                 )
-
         self.logger.info("GNN training process completed.")
 
     def _generate_embeddings(self, model: nn.Module, data: Data) -> torch.Tensor:
@@ -348,3 +343,77 @@ def _generate_embeddings(self, model: nn.Module, data: Data) -> torch.Tensor:
         with torch.no_grad():
             embeddings = model.get_embeddings(data)
         return embeddings.cpu()
+
+    def _tune_helper(self, config):
+        tuned_instance = GNNEmbedding(
+            adjacency_matrix=self.adjacency_matrix,
+            omics_data=self.omics_data,
+            phenotype_data=self.phenotype_data,
+            clinical_data=self.clinical_data,
+            phenotype_col=self.phenotype_col,
+            model_type=config.get("model_type", self.model_type),
+            hidden_dim=config["hidden_dim"],
+            layer_num=config["layer_num"],
+            dropout=config["dropout"],
+            num_epochs=config["num_epochs"],
+            lr=config["lr"],
+            weight_decay=config["weight_decay"],
+            gpu=(self.device.type == "cuda"),
+            random_feature_dim=self.random_feature_dim,
+            seed=None,
+            tune=False,
+        )
+        tuned_instance.fit()
+        node_emb = tuned_instance.embed()
+
+        node_labels = self._prepare_node_labels().values
+        y = (node_labels > node_labels.mean()).astype(int)
+
+        X = node_emb.detach().numpy()
+
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+        clf = RandomForestClassifier(n_estimators=100)
+        clf.fit(X_train, y_train)
+        y_pred = clf.predict(X_test)
+
+        acc = accuracy_score(y_test, y_pred)
+        session.report({"accuracy": acc})
+
+    def run_gnn_embedding_tuning(self, num_samples=10):
+        """
+        Runs hyperparameter tuning for the GNNEmbedding module using Ray Tune
+        with accuracy as classification metric.
+        """
+        config = {
+            "model_type": tune.choice(["GCN", "GAT", "GAT", "SAGE", "GIN"]),
+            "hidden_dim": tune.choice([8, 16, 32, 64, 128]),
+            "layer_num": tune.choice([2, 3, 4, 5]),
+            "dropout": tune.choice([True, False]),
+            "num_epochs": tune.choice([50, 100, 150, 200]),
+            "lr": tune.loguniform(1e-4, 1e-1),
+            "weight_decay": tune.loguniform(1e-4, 1e-1),
+        }
+
+        scheduler = ASHAScheduler(
+            metric="accuracy", mode="max", grace_period=10, reduction_factor=2
+        )
+        reporter = CLIReporter(metric_columns=["accuracy", "training_iteration"])
+
+        def short_dirname_creator(trial):
+            return f"_{trial.trial_id}"
+
+        result = tune.run(
+            tune.with_parameters(self._tune_helper),
+            config=config,
+            num_samples=num_samples,
+            scheduler=scheduler,
+            progress_reporter=reporter,
+            storage_path=os.path.expanduser("~/r"),
+            trial_dirname_creator=short_dirname_creator,
+            name="e",
+        )
+
+        best_trial = result.get_best_trial("accuracy", "max", "last")
+        print("Best trial config:", best_trial.config)
+        print("Best trial final accuracy:", best_trial.last_result["accuracy"])
+        return best_trial.config
diff --git a/bioneuralnet/subject_representation/subject_representation.py b/bioneuralnet/subject_representation/subject_representation.py
index 5589967..e4ce68b 100644
--- a/bioneuralnet/subject_representation/subject_representation.py
+++ b/bioneuralnet/subject_representation/subject_representation.py
@@ -1,8 +1,19 @@
 import pandas as pd
+import numpy as np
 from sklearn.decomposition import PCA
-from typing import Optional
+from typing import Optional, Dict, Any
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from ray import tune
+from ray.tune import CLIReporter
+from ray.tune.schedulers import ASHAScheduler
 from ..utils.logger import get_logger
-from ..network_embedding import GNNEmbedding
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from ray.air import session
 
 
 class GraphEmbedding:
@@ -12,210 +23,290 @@ class GraphEmbedding:
 
     def __init__(
         self,
-        adjacency_matrix: pd.DataFrame,
         omics_data: pd.DataFrame,
-        phenotype_data: pd.DataFrame,
-        clinical_data: Optional[pd.DataFrame] = None,
-        embeddings: Optional[pd.DataFrame] = None,
+        embeddings: pd.DataFrame,
+        phenotype_data: Optional[pd.DataFrame] = None,
+        phenotype_col: str = "phenotype",
         reduce_method: str = "PCA",
+        tune: Optional[bool] = False,
     ):
         """
         Initializes the GraphEmbedding instance.
 
         Parameters:
-            adjacency_matrix : pd.DataFrame
             omics_data : pd.DataFrame
-            phenotype_data : pd.DataFrame
-            clinical_data : Optional[pd.DataFrame], default=None
             embeddings : Optional[pd.DataFrame], default=None
             reduce_method : str, optional
         """
         self.logger = get_logger(__name__)
         self.logger.info("Initializing GraphEmbedding with provided data inputs.")
 
-        if adjacency_matrix is None or adjacency_matrix.empty:
-            raise ValueError("Adjacency matrix is required and cannot be empty.")
         if omics_data is None or omics_data.empty:
             raise ValueError("Omics data must be non-empty.")
-        if phenotype_data is None or phenotype_data.empty:
-            raise ValueError("Phenotype data is required and cannot be empty.")
-
-        if clinical_data is not None and clinical_data.empty:
-            self.logger.warning(
-                "Clinical data provided is empty. Node features will be initialized randomly."
-            )
-            clinical_data = None
 
         if embeddings is None or embeddings.empty:
             self.embedding_method = "GNNs"
             self.logger.info(
-                "No precomputed embeddings provided. Defaulting to GNN-based embedding generation."
+                "No embeddings provided, please review documentation to see how to generate embeddings."
             )
         else:
             self.embedding_method = "precomputed"
             if not isinstance(embeddings, pd.DataFrame):
                 raise ValueError("Embeddings must be provided as a pandas DataFrame.")
-            missing_nodes = set(adjacency_matrix.index) - set(embeddings.index)
-            if missing_nodes:
-                raise ValueError(
-                    f"Provided embeddings are missing nodes: {missing_nodes}"
-                )
+
             self.logger.info(
                 "Precomputed embeddings provided. Skipping GNN-based embedding generation."
             )
 
-        self.adjacency_matrix = adjacency_matrix
+        if tune == True and phenotype_data is None:
+            raise ValueError(
+                "Phenotype data must be provided for classification-based tuning."
+            )
+
         self.omics_data = omics_data
+        self.embeddings = embeddings if embeddings is not None else pd.DataFrame()
         self.phenotype_data = phenotype_data
-        self.clinical_data = clinical_data
-        self.embeddings = embeddings
-        self.reduce_method = reduce_method
+        self.phenotype_col = phenotype_col
+        self.reduce_method = reduce_method.upper()
+        self.tune = tune
 
     def run(self) -> pd.DataFrame:
         """
-        Main pipeline:
-            - Generate (or load) node embeddings.
-            - Reduce them to 1D with the specified reduction method.
-            - Integrate each node's reduced embedding value into the subject-level omics data.
-
-        Returns:
-            pd.DataFrame
-
-        Raises:
-            ValueError
-            Exception
+        If tune=True, perform classification-based tuning (if phenotype_data provided),
+        else fallback to a default embedding reduction method.
         """
         self.logger.info("Starting Subject Representation workflow.")
+
+        if self.embeddings.empty:
+            self.logger.warning(
+                "No embeddings provided. Returning original omics_data."
+            )
+            return self.omics_data
+
         try:
-            embeddings_df = self.generate_embeddings()
-            node_embedding_values = self.reduce_embeddings(embeddings_df)
-            enhanced_omics_data = self.integrate_embeddings(node_embedding_values)
+            if self.tune:
+                best_config = self._run_tuning()
+                self.logger.info(f"Best tuning config selected: {best_config}")
+
+                reduced = self._reduce_embeddings(
+                    method=best_config["method"],
+                    pca_dim=best_config.get("pca_dim", 1),
+                    ae_params=best_config.get(
+                        "ae_params", {"epochs": 16, "hidden_dim": 8}
+                    ),
+                )
+            else:
+                reduced = self._reduce_embeddings(
+                    method=self.reduce_method,
+                    pca_dim=1,
+                    ae_params={"epochs": 16, "hidden_dim": 8},
+                )
 
-            self.logger.info("Subject Representation workflow completed successfully.")
+            if reduced.empty:
+                self.logger.warning(
+                    "Reduced embeddings are empty. Returning original omics_data."
+                )
+                return self.omics_data
+
+            enhanced_omics_data = self._integrate_embeddings(reduced)
+            self.logger.info(
+                f"Subject Representation completed successfully. Final shape: {enhanced_omics_data.shape}"
+            )
             return enhanced_omics_data
 
         except Exception as e:
             self.logger.error(f"Error in Subject Representation workflow: {e}")
             raise
 
-    def generate_embeddings(self) -> pd.DataFrame:
-        """
-        Generate or retrieve node embeddings.
+    def _reduce_embeddings(
+        self, method: str, pca_dim: int = 1, ae_params: Dict[str, Any] = None
+    ) -> pd.Series:
+        self.logger.info(f"Reducing embeddings to 1D using method='{method}'.")
+        if self.embeddings.empty:
+            raise ValueError("Embeddings DataFrame is empty.")
+        if method == "PCA":
+            self.logger.info(f"Applying PCA with n_components={pca_dim}.")
+            pca = PCA(n_components=pca_dim)
+            pcs = pca.fit_transform(self.embeddings)
+            if pca_dim == 1:
+                reduced_embedding = pd.Series(
+                    pcs.flatten(), index=self.embeddings.index, name="PC1"
+                )
+            else:
+                reduced_embedding = pd.Series(
+                    pcs.mean(axis=1), index=self.embeddings.index, name="PC_mean"
+                )
+            self.logger.info(
+                "Captured variance ratio: %.2f" % pca.explained_variance_ratio_[0]
+            )
+            self.logger.info("PCA reduction completed.")
+        # elif method == "AVG":
+        #     self.logger.info("Calculating average of embedding dimensions.")
+        #     reduced_embedding = self.embeddings.mean(axis=1).rename("Avg_Embed")
+        #     self.logger.info("Average reduction completed.")
+        # elif method == "MAX":
+        #     self.logger.info("Calculating maximum of embedding dimensions.")
+        #     reduced_embedding = self.embeddings.max(axis=1).rename("Max_Embed")
+        #     self.logger.info("Maximum reduction completed.")
+        elif method == "AE":
+            self.logger.info("Using Autoencoder for reduction.")
+            if ae_params is None:
+                ae_params = {"epochs": 16, "hidden_dim": 8}
+            input_dim = self.embeddings.shape[1]
+            X = torch.tensor(self.embeddings.values, dtype=torch.float)
+            model = SimpleAE(
+                input_dim=input_dim,
+                hidden_dim=ae_params.get("hidden_dim", 16),
+                compressed_dim=1,
+            )
+            optimizer = optim.Adam(model.parameters(), lr=1e-3)
+            loss_fn = nn.MSELoss()
+            model.train()
+            for epoch in range(ae_params.get("epochs", 10)):
+                optimizer.zero_grad()
+                z, recon = model(X)
+                loss = loss_fn(recon, X)
+                loss.backward()
+                optimizer.step()
+            model.eval()
+            with torch.no_grad():
+                z, _ = model(X)
+            reduced_embedding = pd.Series(
+                z.squeeze().numpy(), index=self.embeddings.index, name="AE"
+            )
+            self.logger.info("Autoencoder reduction completed.")
+        else:
+            self.logger.error(f"Unsupported reduction method: {method}")
+            raise ValueError(f"Unsupported reduction method: {method}")
 
-        Returns:
-            pd.DataFrame
-        Raises:
-            ValueError
-        """
-        self.logger.info(
-            f"Generating embeddings using method='{self.embedding_method}'."
+        reduced_embedding = (reduced_embedding - reduced_embedding.mean()) / (
+            reduced_embedding.std() + 1e-8
         )
 
-        if self.embedding_method == "precomputed":
-            self.logger.info("Using precomputed embeddings.")
-            # making sure embeddings are not None, or precommit will fail
-            if self.embeddings is None:
-                raise ValueError("Embeddings are None.")
-            return self.embeddings.copy()
+        self.logger.info("Reduced embedding normalized.")
+        return reduced_embedding
 
-        else:
-            self.logger.info("Generating embeddings using GNNEmbedding.")
-            # for the other parameters it will just use default values
-            gnn_embedder = GNNEmbedding(
-                adjacency_matrix=self.adjacency_matrix,
-                omics_data=self.omics_data,
-                phenotype_data=self.phenotype_data,
-                clinical_data=self.clinical_data,
-            )
+    def _integrate_embeddings(
+        self, reduced: pd.Series, method="multiply"
+    ) -> pd.DataFrame:
+        self.logger.info(f"Integrating embeddings using method='{method}'.")
 
-            gnn_embedder.fit()
-            embeddings_tensor = gnn_embedder.embed()
+        common = list(set(self.omics_data.columns).intersection(set(reduced.index)))
+        if not common:
+            raise ValueError("No common features between omics data and embeddings.")
 
-            node_names = self.adjacency_matrix.index.tolist()
-            embeddings_df = pd.DataFrame(
-                embeddings_tensor.numpy(),
-                index=node_names,
-                columns=[f"Embed_{i+1}" for i in range(embeddings_tensor.shape[1])],
-            )
+        enhanced_omics = self.omics_data.copy()
+
+        if method == "multiply":
+            for feature in common:
+                enhanced_omics[feature] = enhanced_omics[feature] * reduced[feature]
+            enhanced_omics = enhanced_omics[common]
             self.logger.info(
-                f"Generated GNN-based embeddings with shape {embeddings_df.shape}."
+                "Integration using multiplication completed. (Columns overwritten)"
             )
-            return embeddings_df
 
-    def reduce_embeddings(self, embeddings: pd.DataFrame) -> pd.Series:
-        """
-        Reduce embeddings to a single dimension per node using the specified method.
+        elif method == "concatenate":
+            enhanced_features = pd.DataFrame(
+                np.tile(reduced.values, (self.omics_data.shape[0], 1)),
+                index=self.omics_data.index,
+                columns=[f"{feat}_embed" for feat in reduced.index],
+            )
+            enhanced_omics = pd.concat([enhanced_omics, enhanced_features], axis=1)
+            self.logger.info("Integration using concatenation completed.")
 
-        Parameters:
-            embeddings : pd.DataFrame
+        elif method == "weighted":
+            weights = np.abs(reduced.values) / (np.sum(np.abs(reduced.values)) + 1e-8)
+            for feature, weight in zip(common, weights):
+                enhanced_omics[f"{feature}_embed"] = (
+                    enhanced_omics[feature] + weight * reduced[feature]
+                )
+            self.logger.info("Integration using weighted sum completed.")
+
+        else:
+            raise ValueError(f"Unknown integration method: {method}")
 
-        Returns:
-            pd.Series
+        self.logger.info(f"Final Enhanced Omics Shape: {enhanced_omics.shape}")
+        return enhanced_omics
 
-        Raises:
-            ValueError
+    def _run_tuning(self) -> Dict[str, Any]:
+        """
+        Classification-based tuning
         """
-        self.logger.info(
-            f"Reducing embeddings to 1D using method='{self.reduce_method}'."
-        )
 
-        if embeddings.empty:
-            raise ValueError("Embeddings DataFrame is empty.")
+        self.logger.info("Running classification-based tuning for GraphEmbedding.")
+        return self._run_classification_tuning()
 
-        if self.reduce_method.upper() == "PCA":
-            self.logger.info("Applying PCA to reduce embeddings to 1D.")
-            pca = PCA(n_components=1, random_state=42)
-            principal_components = pca.fit_transform(embeddings)
-            reduced_embedding = pd.Series(
-                principal_components.flatten(), index=embeddings.index, name="PC1"
+    def _run_classification_tuning(self) -> Dict[str, Any]:
+        search_config = {
+            "method": tune.choice(["PCA", "AE"]),
+            "pca_dim": tune.choice([1, 2, 3]),
+            "ae_params": tune.choice(
+                [
+                    {"epochs": 64, "hidden_dim": 8},
+                    {"epochs": 128, "hidden_dim": 8},
+                    {"epochs": 256, "hidden_dim": 4},
+                ]
+            ),
+            "integration_method": tune.choice(["multiply"]),
+        }
+
+        def tune_helper(config):
+            reduced = self._reduce_embeddings(
+                config["method"], config["pca_dim"], config["ae_params"]
             )
-            self.logger.info("PCA reduction completed.")
-        elif self.reduce_method.upper() == "AVG":
-            self.logger.info("Calculating average of embedding dimensions.")
-            reduced_embedding = embeddings.mean(axis=1)
-            reduced_embedding.name = "Avg_Embed"
-            self.logger.info("Average reduction completed.")
-        elif self.reduce_method.upper() == "MAX":
-            self.logger.info("Calculating maximum of embedding dimensions.")
-            reduced_embedding = embeddings.max(axis=1)
-            reduced_embedding.name = "Max_Embed"
-            self.logger.info("Maximum reduction completed.")
-        else:
-            self.logger.error(f"Unsupported reduction method: {self.reduce_method}")
-            raise ValueError(f"Unsupported reduction method: {self.reduce_method}")
+            if config["integration_method"] == "multiply":
+                enhanced = self._integrate_embeddings(reduced, method="multiply")
+            # elif config["integration_method"] == "concatenate":
+            #     enhanced = self._integrate_embeddings(reduced, method="concatenate")
+            # elif config["integration_method"] == "weighted":
+            #     enhanced = self._integrate_embeddings(reduced, method="weighted")
+            else:
+                raise ValueError("Unknown integration method")
 
-        return reduced_embedding
+            common_samples = enhanced.index.intersection(self.phenotype_data.index)
+            X = enhanced.loc[common_samples].values
+            y = self.phenotype_data.loc[common_samples, self.phenotype_col].values
+            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+            clf = RandomForestClassifier(n_estimators=100)
+            clf.fit(X_train, y_train)
+            y_pred = clf.predict(X_test)
+            acc = accuracy_score(y_test, y_pred)
+            session.report({"accuracy": acc})
 
-    def integrate_embeddings(self, node_embedding_values: pd.Series) -> pd.DataFrame:
-        """
-        Integrate the reduced node embeddings into the subject-level omics data.
-        """
-        self.logger.info(
-            "Integrating reduced node embeddings into subject-level omics data."
-        )
+        scheduler = ASHAScheduler(metric="accuracy", mode="max", grace_period=10)
+        reporter = CLIReporter(metric_columns=["accuracy", "training_iteration"])
 
-        try:
-            feature_cols = self.omics_data.columns
-            missing_nodes = set(feature_cols) - set(node_embedding_values.index)
-            if missing_nodes:
-                self.logger.warning(
-                    f"Some omics features have no corresponding embeddings: {missing_nodes}"
-                )
+        def short_dirname_creator(trial):
+            return f"_{trial.trial_id}"
 
-            enhanced_omics = self.omics_data.copy()
-            for node in feature_cols:
-                if node in node_embedding_values.index:
-                    enhanced_omics[node] = (
-                        enhanced_omics[node] * node_embedding_values[node]
-                    )
-                else:
-                    self.logger.warning(
-                        f"No embedding found for feature '{node}'. Skipping integration for this feature."
-                    )
+        analysis = tune.run(
+            tune_helper,
+            config=search_config,
+            num_samples=10,
+            scheduler=scheduler,
+            progress_reporter=reporter,
+            trial_dirname_creator=short_dirname_creator,
+            name="tune",
+        )
+        best_trial = analysis.get_best_trial("accuracy", "max", "last")
+        return best_trial.config
 
-            self.logger.info("Integration of embeddings completed successfully.")
-            return enhanced_omics
 
-        except Exception as e:
-            self.logger.error(f"Error during integration of embeddings: {e}")
-            raise
+class SimpleAE(nn.Module):
+    def __init__(self, input_dim, hidden_dim=16, compressed_dim=1):
+        super(SimpleAE, self).__init__()
+        self.encoder = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, compressed_dim),
+        )
+        self.decoder = nn.Sequential(
+            nn.Linear(compressed_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, input_dim),
+        )
+
+    def forward(self, x):
+        z = self.encoder(x)
+        recon = self.decoder(z)
+        return z, recon

From 27e201ee17cf53040970081e7a1ef015b602bb66 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 6 Feb 2025 07:35:28 -0700
Subject: [PATCH 23/26] Added hyperparameter tunning to correalted page rank
 and correalted lovain: Manual check

---
 bioneuralnet/clustering/__init__.py           |   7 +-
 bioneuralnet/clustering/correlated_louvain.py | 191 +++++++++++-
 .../clustering/correlated_pagerank.py         | 274 +++++++++--------
 bioneuralnet/clustering/hybrid_louvain.py     | 286 ++++++++++++++++++
 4 files changed, 610 insertions(+), 148 deletions(-)
 create mode 100644 bioneuralnet/clustering/hybrid_louvain.py

diff --git a/bioneuralnet/clustering/__init__.py b/bioneuralnet/clustering/__init__.py
index 15ac6af..5c8a94f 100644
--- a/bioneuralnet/clustering/__init__.py
+++ b/bioneuralnet/clustering/__init__.py
@@ -1,4 +1,5 @@
-from .correlated_pagerank import PageRank
-from .correlated_louvain import Louvain
+from .correlated_pagerank import CorrelatedPageRank
+from .correlated_louvain import CorrelatedLouvain
+from .hybrid_louvain import HybridLouvain
 
-__all__ = ["PageRank", "Louvain"]
+__all__ = ["CorrelatedPageRank", "CorrelatedLouvain", "HybridLouvain"]
diff --git a/bioneuralnet/clustering/correlated_louvain.py b/bioneuralnet/clustering/correlated_louvain.py
index 0da242f..fdb8f8e 100644
--- a/bioneuralnet/clustering/correlated_louvain.py
+++ b/bioneuralnet/clustering/correlated_louvain.py
@@ -1,20 +1,189 @@
+import numpy as np
+import networkx as nx
 import pandas as pd
+from community.community_louvain import (
+    modularity as original_modularity,
+    best_partition,
+)
+from scipy.stats import pearsonr
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from ..utils.logger import get_logger
 
 
-class Louvain:
-    """
-    Correlated Louvain:
+from ray import tune
+from ray.tune import CLIReporter
+from ray.air import session
+from ray.tune.schedulers import ASHAScheduler
 
-    Attributes:
-    """
+logger = get_logger(__name__)
 
-    def __init__(self, data: pd.DataFrame):
-        self.data = data
 
-    def run(self) -> pd.DataFrame:
+class CorrelatedLouvain:
+    def __init__(
+        self,
+        G: nx.Graph,
+        B: pd.DataFrame,
+        Y=None,
+        k3: float = 0.2,
+        k4: float = 0.8,
+        weight: str = "weight",
+        tune: bool = False,
+    ):
+        self.logger = get_logger(__name__)
+        self.G = G.copy()
+        self.B = B.copy()
+        self.Y = Y
+        self.K3 = k3
+        self.K4 = k4
+        self.weight = weight
+        self.tune = tune
+
+        self.logger.info(
+            f"Initialized CorrelatedLouvain with k3 = {self.K3}, k4 = {self.K4}, "
+        )
+        if self.B is not None:
+            self.logger.info(f"Original omics data shape: {self.B.shape}")
+
+        self.logger.info(f"Original graph has {self.G.number_of_nodes()} nodes.")
+
+        if self.B is not None:
+            self.logger.info(f"Final omics data shape: {self.B.shape}")
+        self.logger.info(
+            f"Graph has {self.G.number_of_nodes()} nodes and {self.G.number_of_edges()} edges."
+        )
+
+    def _compute_community_correlation(self, nodes) -> tuple:
         """
+        Compute the Pearson correlation between the first principal component (PC1) of the omics data
+        (for the given nodes) and the phenotype.
+        Drops columns that are completely zero.
+        """
+        try:
+            self.logger.info(
+                f"Computing community correlation for {len(nodes)} nodes..."
+            )
+            node_cols = [str(n) for n in nodes if str(n) in self.B.columns]
+            if not node_cols:
+                self.logger.info(
+                    "No valid columns found for these nodes; returning (0.0, 1.0)."
+                )
+                return 0.0, 1.0
+            B_sub = self.B.loc[:, node_cols]
+            zero_mask = (B_sub == 0).all(axis=0)
+            num_zero_columns = int(zero_mask.sum())
+            if num_zero_columns > 0:
+                self.logger.info(
+                    f"WARNING: {num_zero_columns} columns are all zeros in community subset."
+                )
+            B_sub = B_sub.loc[:, ~zero_mask]
+            if B_sub.shape[1] == 0:
+                self.logger.info("All columns dropped; returning (0.0, 1.0).")
+                return 0.0, 1.0
+
+            self.logger.info(
+                f"B_sub shape: {B_sub.shape}, first few columns: {node_cols[:5]}"
+            )
+            scaler = StandardScaler()
+            scaled = scaler.fit_transform(B_sub)
+            pca = PCA(n_components=1)
+            pc1 = pca.fit_transform(scaled).flatten()
+            target = (
+                self.Y.iloc[:, 0].values
+                if isinstance(self.Y, pd.DataFrame)
+                else self.Y.values
+            )
+            corr, pvalue = pearsonr(pc1, target)
+            return corr, pvalue
+        except Exception as e:
+            self.logger.info(f"Error in _compute_community_correlation: {e}")
+            raise
 
-        Returns:
-            pd.DataFrame:
+    def _quality_correlated(self, partition) -> float:
+        """
+        Compute the overall quality metric as:
+            Q* = k3 * Q + k4 * avg_abs_corr,
+        where Q is the standard modularity and avg_abs_corr is the average absolute Pearson correlation
+        (computed over communities with at least 2 nodes).
         """
-        return self.data
+        Q = original_modularity(partition, self.G, self.weight)
+        if self.B is None or self.Y is None:
+            self.logger.info(
+                "Omics/phenotype data not provided; returning standard modularity."
+            )
+            return Q
+        community_corrs = []
+        for com in set(partition.values()):
+            nodes = [n for n in self.G.nodes() if partition[n] == com]
+            if len(nodes) < 2:
+                continue
+            corr, _ = self._compute_community_correlation(nodes)
+            community_corrs.append(abs(corr))
+        avg_corr = np.mean(community_corrs) if community_corrs else 0.0
+        quality = self.K3 * Q + self.K4 * avg_corr
+        self.logger.info(
+            f"Computed quality: Q = {Q:.4f}, avg_corr = {avg_corr:.4f}, combined = {quality:.4f}"
+        )
+        return quality
+
+    def run(self) -> dict:
+        if self.tune:
+            self.logger.info("Tuning enabled. Running hyperparameter tuning...")
+            best_config = self.run_tuning(num_samples=5)
+            self.logger.info("Tuning completed successfully.")
+            return {"best_config": best_config}
+        else:
+            self.logger.info("Running standard community detection...")
+            partition = best_partition(self.G, weight=self.weight)
+            quality = self._quality_correlated(partition)
+            self.logger.info(f"Final quality: {quality:.4f}")
+            self.partition = partition
+            return partition
+
+    def get_quality(self) -> float:
+        if not hasattr(self, "partition"):
+            raise ValueError("No partition computed. Call run() first.")
+        return self._quality_correlated(self.partition)
+
+    def _tune_helper(self, config):
+        k4 = config["k4"]
+        k3 = 1.0 - k4
+        tuned_instance = CorrelatedLouvain(
+            G=self.G,
+            B=self.B,
+            Y=self.Y,
+            k3=k3,
+            k4=k4,
+            weight=self.weight,
+            tune=False,
+        )
+        tuned_instance.run()
+        quality = tuned_instance.get_quality()
+        session.report({"quality": quality})
+
+    def run_tuning(self, num_samples=10):
+        search_config = {"k4": tune.grid_search([0.5, 0.6, 0.7, 0.8, 0.9])}
+        scheduler = ASHAScheduler(
+            metric="quality",
+            mode="max",
+            grace_period=1,
+            reduction_factor=2,
+        )
+        reporter = CLIReporter(metric_columns=["quality", "training_iteration"])
+
+        def short_dirname_creator(trial):
+            return f"_{trial.trial_id}"
+
+        analysis = tune.run(
+            tune.with_parameters(self._tune_helper),
+            config=search_config,
+            num_samples=num_samples,
+            scheduler=scheduler,
+            progress_reporter=reporter,
+            trial_dirname_creator=short_dirname_creator,
+            name="l",
+        )
+
+        best_config = analysis.get_best_config(metric="quality", mode="max")
+        self.logger.info(f"Best hyperparameters found: {best_config}")
+        return best_config
diff --git a/bioneuralnet/clustering/correlated_pagerank.py b/bioneuralnet/clustering/correlated_pagerank.py
index 0c32dda..fc5ba5d 100644
--- a/bioneuralnet/clustering/correlated_pagerank.py
+++ b/bioneuralnet/clustering/correlated_pagerank.py
@@ -1,6 +1,4 @@
-import os
-from typing import List, Tuple, Dict, Any, Optional
-from datetime import datetime
+from typing import List, Tuple, Dict, Any
 import pandas as pd
 import networkx as nx
 from sklearn.preprocessing import StandardScaler
@@ -8,8 +6,13 @@
 from scipy.stats import pearsonr
 from ..utils.logger import get_logger
 
+from ray import tune
+from ray.tune import CLIReporter
+from ray.air import session
+from ray.tune.schedulers import ASHAScheduler
 
-class PageRank:
+
+class CorrelatedPageRank:
     """
     PageRank Class for Clustering Nodes Based on Personalized PageRank.
 
@@ -28,12 +31,12 @@ def __init__(
         self,
         graph: nx.Graph,
         omics_data: pd.DataFrame,
-        phenotype_data: pd.Series,
+        phenotype_data: pd.DataFrame,
         alpha: float = 0.9,
         max_iter: int = 100,
         tol: float = 1e-6,
-        k: float = 0.9,
-        output_dir: Optional[str] = None,
+        k: float = 0.5,
+        tune: bool = False,
     ):
         """
         Initializes the PageRank instance with direct data structures.
@@ -41,21 +44,20 @@ def __init__(
         Args:
             graph (nx.Graph): NetworkX graph object representing the network.
             omics_data (pd.DataFrame): Omics data DataFrame.
-            phenotype_data (pd.Series): Phenotype data Series.
+            phenotype_data (pd.DataFrame): Phenotype data Series.
             alpha (float, optional): Damping factor for PageRank. Defaults to 0.9.
             max_iter (int, optional): Maximum iterations for PageRank. Defaults to 100.
             tol (float, optional): Tolerance for convergence. Defaults to 1e-6.
             k (float, optional): Weighting factor for composite score. Defaults to 0.9.
-            output_dir (str, optional): Directory to save outputs. If None, creates a unique directory.
         """
         self.G = graph
         self.B = omics_data
-        self.Y = phenotype_data
+        self.Y = phenotype_data.squeeze()
         self.alpha = alpha
         self.max_iter = max_iter
         self.tol = tol
         self.k = k
-        self.output_dir = output_dir  # if output_dir else self._create_output_dir()
+        self.tune = tune
 
         self.logger = get_logger(__name__)
         self.logger.info("Initialized PageRank with the following parameters:")
@@ -68,8 +70,6 @@ def __init__(
         self.logger.info(f"Max Iterations: {self.max_iter}")
         self.logger.info(f"Tolerance: {self.tol}")
         self.logger.info(f"K (Composite Score Weight): {self.k}")
-        self.logger.info(f"Output Directory: {self.output_dir}")
-
         self._validate_inputs()
 
     def _validate_inputs(self):
@@ -98,20 +98,6 @@ def _validate_inputs(self):
             self.logger.error(f"Input validation error: {e}")
             raise
 
-    def _create_output_dir(self) -> str:
-        """
-        Creates a unique output directory for the current PageRank run.
-
-        Returns:
-            str: Path to the created output directory.
-        """
-        base_dir = "pagerank_output"
-        timestamp = datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
-        output_dir = f"{base_dir}_{timestamp}"
-        os.makedirs(output_dir, exist_ok=True)
-        self.logger.info(f"Created output directory: {output_dir}")
-        return output_dir
-
     def phen_omics_corr(self, nodes: List[Any]) -> Tuple[float, str]:
         """
         Calculates the Pearson correlation between the PCA of omics data and phenotype.
@@ -145,28 +131,14 @@ def phen_omics_corr(self, nodes: List[Any]) -> Tuple[float, str]:
     def sweep_cut(
         self, p: Dict[Any, float]
     ) -> Tuple[List[Any], int, float, float, float, str]:
-        """
-        Performs sweep cut based on the PageRank scores.
-
-        Args:
-            p (Dict[Any, float]): Dictionary of PageRank scores.
-
-        Returns:
-            Tuple containing:
-                - List of node identifiers in the cluster.
-                - Cluster size.
-                - Conductance.
-                - Correlation.
-                - Composite score.
-                - Correlation with p-value.
-        """
         try:
-            cond_res = []
-            corr_res = []
-            cond_corr_res = []
-            cluster = set()
-            min_cut, min_cond_corr = len(p), float("inf")
-            len_clus, cond, corr, cor_pval = 0, 1, 0.0, ""
+            best_cluster = set()
+            min_comp_score = float("inf")
+            best_len = 0
+            best_cond = 1.0
+            best_corr = 0.0
+            best_corr_pval = ""
+
             degrees = dict(self.G.degree(weight="weight"))
             vec = sorted(
                 [
@@ -176,42 +148,65 @@ def sweep_cut(
                 reverse=True,
             )
 
+            current_cluster = set()
             for i, (val, node) in enumerate(vec):
+                self.logger.debug(
+                    f"Iteration {i}: Adding node {node} with norm value {val:.3f}"
+                )
                 if val == 0:
-                    break
-                else:
-                    cluster.add(node)
+                    continue
+
+                current_cluster.add(node)
+
+                if len(current_cluster) < len(self.G.nodes()):
+                    vol_S = sum(
+                        d for n, d in self.G.degree(current_cluster, weight="weight")
+                    )
+                    vol_T = sum(
+                        d
+                        for n, d in self.G.degree(
+                            set(self.G.nodes()) - current_cluster, weight="weight"
+                        )
+                    )
 
-                if len(self.G.nodes()) > len(cluster):
-                    cluster_cond = nx.conductance(self.G, cluster, weight="weight")
-                    cond_res.append(round(cluster_cond, 3))
+                    if min(vol_S, vol_T) == 0:
+                        self.logger.warning(
+                            f"Skipping iteration {i} due to zero volume (vol_S={vol_S}, vol_T={vol_T})."
+                        )
+                        continue
 
-                    Nodes = list(cluster)
-                    cluster_corr, corr_pvalue = self.phen_omics_corr(Nodes)
-                    corr_res.append(round(cluster_corr, 3))
-                    cluster_corr_neg = -abs(round(cluster_corr, 3))
+                    cluster_cond = nx.conductance(
+                        self.G, current_cluster, weight="weight"
+                    )
+                    cluster_corr, corr_pvalue = self.phen_omics_corr(
+                        list(current_cluster)
+                    )
+                    composite_score = (1 - self.k) * cluster_cond + self.k * (
+                        -abs(cluster_corr)
+                    )
 
-                    cond_corr = round(
-                        (1 - self.k) * cluster_cond + self.k * cluster_corr_neg, 3
+                    self.logger.debug(
+                        f"Cluster size={len(current_cluster)}, cond={cluster_cond:.3f}, "
+                        f"corr={cluster_corr:.3f}, composite={composite_score:.3f}"
                     )
-                    cond_corr_res.append(cond_corr)
 
-                    if cond_corr < min_cond_corr:
-                        min_cond_corr, min_cut = cond_corr, i
-                        len_clus = len(cluster)
-                        cond = cluster_cond
-                        corr = cluster_corr
-                        cor_pval = corr_pvalue
+                    # cluster must be larger than 10 nodes
+                    if len(current_cluster) >= 5 and composite_score < min_comp_score:
+                        min_comp_score = composite_score
+                        best_cluster = set(current_cluster)
+                        best_len = len(current_cluster)
+                        best_cond = cluster_cond
+                        best_corr = cluster_corr
+                        best_corr_pval = corr_pvalue
 
-            if min_cut < len(vec):
-                nodes_in_cluster = [vec[i][1] for i in range(min_cut + 1)]
+            if best_cluster:
                 return (
-                    nodes_in_cluster,
-                    len_clus,
-                    cond,
-                    corr,
-                    round(min_cond_corr, 3),
-                    cor_pval,
+                    list(best_cluster),
+                    best_len,
+                    round(best_cond, 3),
+                    round(best_corr, 3),
+                    round(min_comp_score, 3),
+                    best_corr_pval,
                 )
             else:
                 self.logger.warning(
@@ -314,8 +309,6 @@ def run_pagerank_clustering(self, seed_nodes: List[Any]) -> Dict[str, Any]:
                 "correlation_pvalue": pval,
             }
 
-            if self.output_dir is not None:
-                self.save_results(results)
             return results
 
         except Exception as e:
@@ -326,46 +319,9 @@ def run_pagerank_clustering(self, seed_nodes: List[Any]) -> Dict[str, Any]:
             self.logger.error(f"Error in run_pagerank_clustering: {e}")
             raise
 
-    def save_results(self, results: Dict[str, Any]) -> None:
-        """
-        Saves the clustering results to a CSV file.
-
-        Args:
-            results (Dict[str, Any]): Clustering results dictionary.
-        """
-        try:
-            timestamp = datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
-            filename = os.path.join(
-                self.output_dir if self.output_dir is not None else "",
-                f"pagerank_results_{timestamp}.csv",
-            )
-
-            df = pd.DataFrame(
-                {
-                    "Node": results["cluster_nodes"],
-                    "Cluster Size": [results["cluster_size"]]
-                    * len(results["cluster_nodes"]),
-                    "Conductance": [results["conductance"]]
-                    * len(results["cluster_nodes"]),
-                    "Correlation": [results["correlation"]]
-                    * len(results["cluster_nodes"]),
-                    "Composite Score": [results["composite_score"]]
-                    * len(results["cluster_nodes"]),
-                    "Correlation P-Value": [results["correlation_pvalue"]]
-                    * len(results["cluster_nodes"]),
-                }
-            )
-
-            df.to_csv(filename, index=False)
-            self.logger.info(f"Clustering results saved to {filename}")
-
-        except Exception as e:
-            self.logger.error(f"Error in save_results: {e}")
-            raise
-
     def run(self, seed_nodes: List[Any]) -> Dict[str, Any]:
         """
-        Executes the PageRank-based clustering pipeline.
+        Executes the correlated PageRank clustering pipeline.
 
         **Steps:**
 
@@ -404,30 +360,80 @@ def run(self, seed_nodes: List[Any]) -> Dict[str, Any]:
             - The PageRank algorithm requires a well-defined and connected graph to produce meaningful results.
             - Results are sensitive to the alpha (damping factor) and other hyperparameters.
 
-        Example:
-
-        .. code-block:: python
-
-            from bioneuralnet.clustering import PageRank
-
-            # Initialize the PageRank clustering instance
-            pagerank_clustering = PageRank(graph=my_graph, alpha=0.9, max_iter=100, tol=1e-6)
-
-            # Define seed nodes for personalization
-            seed_nodes = ['node1', 'node2', 'node3']
-
-            # Run the clustering pipeline
-            results = pagerank_clustering.run(seed_nodes=seed_nodes)
-
-            # Output the results
-            print("Clusters:", results['clusters'])
-            print("Scores:", results['scores'])
         """
+        if self.tune:
+            best_config = self.run_tuning(num_samples=10)
+            self.logger.info("Tuning completed successfully.")
+            return {"best_config": best_config}
         try:
             results = self.run_pagerank_clustering(seed_nodes)
             self.logger.info("PageRank clustering completed successfully.")
             return results
-
         except Exception as e:
             self.logger.error(f"Error in run method: {e}")
             raise
+
+    def get_quality(self) -> float:
+        """
+        Returns the composite score (or correlation) from the latest clustering run.
+        """
+        if hasattr(self, "results"):
+            #  return the composite score
+            return self.results.get("composite_score", 0.0)
+        else:
+            # run clustering on all nodes and then return the score.
+            self.results = self.run_pagerank_clustering(seed_nodes=list(self.G.nodes()))
+            return self.results.get("composite_score", 0.0)
+
+    def _tune_helper(self, config):
+        alpha = config["alpha"]
+        max_iter = config["max_iter"]
+        tol = config["tol"]
+        k = config["k"]
+
+        tuned_instance = CorrelatedPageRank(
+            graph=self.G,
+            omics_data=self.B,
+            phenotype_data=self.Y,
+            alpha=alpha,
+            max_iter=max_iter,
+            tol=tol,
+            k=k,
+            tune=False,
+        )
+        #  tuning uses all nodes as seed nodes.
+        tuned_instance.run_pagerank_clustering(seed_nodes=list(self.G.nodes()))
+        quality = tuned_instance.get_quality()
+        session.report({"quality": quality})
+
+    def run_tuning(self, num_samples: int = 10) -> Dict[str, Any]:
+        search_config = {
+            "alpha": tune.grid_search([0.8, 0.85, 0.9, 0.95]),
+            "k": tune.grid_search([0.5, 0.6, 0.7, 0.8]),
+            "max_iter": tune.choice([100, 500, 1000]),
+            "tol": tune.loguniform(1e-3, 1e-5),
+        }
+        scheduler = ASHAScheduler(
+            metric="quality",
+            mode="max",
+            grace_period=1,
+            reduction_factor=2,
+        )
+        reporter = CLIReporter(metric_columns=["quality", "training_iteration"])
+
+        def short_dirname_creator(trial):
+            return f"_{trial.trial_id}"
+
+        analysis = tune.run(
+            tune.with_parameters(self._tune_helper),
+            config=search_config,
+            num_samples=num_samples,
+            scheduler=scheduler,
+            progress_reporter=reporter,
+            trial_dirname_creator=short_dirname_creator,
+            name="l",
+        )
+
+        best_config = analysis.get_best_config(metric="quality", mode="max")
+        self.logger.info(f"Best hyperparameters found: {best_config}")
+        return best_config
diff --git a/bioneuralnet/clustering/hybrid_louvain.py b/bioneuralnet/clustering/hybrid_louvain.py
new file mode 100644
index 0000000..c108711
--- /dev/null
+++ b/bioneuralnet/clustering/hybrid_louvain.py
@@ -0,0 +1,286 @@
+import networkx as nx
+import pandas as pd
+
+from bioneuralnet.clustering.correlated_pagerank import CorrelatedPageRank
+from bioneuralnet.clustering.correlated_louvain import CorrelatedLouvain
+
+from ..utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class HybridLouvain:
+    def __init__(
+        self,
+        G: nx.Graph,
+        B: pd.DataFrame,
+        Y,
+        k3: float = 0.2,
+        k4: float = 0.8,
+        max_iter: int = 10,
+        weight: str = "weight",
+        tune: bool = False,
+    ):
+        self.logger = get_logger(__name__)
+        self.G = G
+        self.B = B
+        self.Y = Y
+        self.k3 = k3
+        self.k4 = k4
+        self.weight = weight
+        self.max_iter = max_iter
+        self.tune = tune
+        self.logger.info(
+            f"Initialized HybridLouvain with max_iter={max_iter}, k3={k3}, k4={k4}, tune={tune}"
+        )
+
+    def run(self) -> dict:
+        iteration = 0
+        prev_size = len(self.G.nodes())
+        current_partition = None
+        all_clusters = {}
+
+        while iteration < self.max_iter:
+            self.logger.info(
+                f"\nIteration {iteration+1}/{self.max_iter}: Running Correlated Louvain..."
+            )
+
+            if self.tune:
+                self.logger.info("Tuning Correlated Louvain for current iteration...")
+                louvain_tuner = CorrelatedLouvain(
+                    self.G,
+                    B=self.B,
+                    Y=self.Y,
+                    k3=self.k3,
+                    k4=self.k4,
+                    weight=self.weight,
+                    tune=True,
+                )
+                best_config_louvain = louvain_tuner.run_tuning(num_samples=5)
+
+                tuned_k4 = best_config_louvain.get("k4", self.k4)
+                tuned_k3 = 1.0 - tuned_k4
+                self.logger.info(
+                    f"Using tuned Louvain parameters: k3={tuned_k3}, k4={tuned_k4}"
+                )
+                louvain = CorrelatedLouvain(
+                    self.G,
+                    B=self.B,
+                    Y=self.Y,
+                    k3=tuned_k3,
+                    k4=tuned_k4,
+                    weight=self.weight,
+                    tune=False,
+                )
+            else:
+                louvain = CorrelatedLouvain(
+                    self.G,
+                    B=self.B,
+                    Y=self.Y,
+                    k3=self.k3,
+                    k4=self.k4,
+                    weight=self.weight,
+                    tune=False,
+                )
+
+            partition = louvain.run()
+            quality_val = louvain.get_quality()
+            self.logger.info(
+                f"Iteration {iteration+1}: Louvain Quality = {quality_val:.4f}"
+            )
+            current_partition = partition
+
+            best_corr = 0
+            best_seed = None
+            for com in set(partition.values()):
+                nodes = [n for n in self.G.nodes() if partition[n] == com]
+                if len(nodes) < 2:
+                    continue
+                try:
+                    corr, _ = louvain._compute_community_correlation(nodes)
+                    if abs(corr) > abs(best_corr):
+                        best_corr = corr
+                        best_seed = nodes
+                except Exception as e:
+                    self.logger.info(
+                        f"Error computing correlation for community {com}: {e}"
+                    )
+            if best_seed is None:
+                self.logger.info("No valid seed community found; stopping iterations.")
+                break
+            self.logger.info(
+                f"Selected seed community of size {len(best_seed)} with correlation {best_corr:.4f}"
+            )
+
+            if self.tune:
+                self.logger.info("Tuning Correlated PageRank for current iteration...")
+                pagerank_tuner = CorrelatedPageRank(
+                    graph=self.G,
+                    omics_data=self.B,
+                    phenotype_data=self.Y,
+                    alpha=0.9,
+                    max_iter=100,
+                    tol=1e-6,
+                    k=0.5,
+                    tune=True,
+                )
+                best_config_pr = pagerank_tuner.run_tuning(num_samples=5)
+                tuned_alpha = best_config_pr.get("alpha", 0.9)
+                tuned_max_iter = best_config_pr.get("max_iter", 100)
+                tuned_tol = best_config_pr.get("tol", 1e-6)
+                tuned_k = best_config_pr.get("k", 0.5)
+                self.logger.info(
+                    f"Using tuned PageRank parameters: alpha={tuned_alpha}, max_iter={tuned_max_iter}, tol={tuned_tol}, k={tuned_k}"
+                )
+                pagerank_instance = CorrelatedPageRank(
+                    graph=self.G,
+                    omics_data=self.B,
+                    phenotype_data=self.Y,
+                    alpha=tuned_alpha,
+                    max_iter=tuned_max_iter,
+                    tol=tuned_tol,
+                    k=tuned_k,
+                    tune=False,
+                )
+            else:
+                pagerank_instance = CorrelatedPageRank(
+                    graph=self.G, omics_data=self.B, phenotype_data=self.Y, tune=False
+                )
+
+            pagerank_results = pagerank_instance.run(best_seed)
+            refined_nodes = pagerank_results.get("cluster_nodes", [])
+            new_size = len(refined_nodes)
+            all_clusters[iteration] = refined_nodes
+            self.logger.info(f"Refined subgraph size: {new_size}")
+            if new_size == prev_size or new_size <= 1:
+                self.logger.info(
+                    "Subgraph size converged or too small. Stopping iterations."
+                )
+                break
+            prev_size = new_size
+            self.G = self.G.subgraph(refined_nodes).copy()
+            iteration += 1
+
+        self.logger.info(f"Hybrid Louvain completed after {iteration+1} iterations.")
+        return {"curr": current_partition, "clus": all_clusters}
+
+
+## Previous implementation before introducing hyperparameter tuning
+
+
+# import networkx as nx
+# import pandas as pd
+# import numpy as np
+
+# from bioneuralnet.clustering.correlated_pagerank import CorrelatedPageRank
+# from bioneuralnet.clustering.correlated_louvain import CorrelatedLouvain
+
+# from ..utils.logger import get_logger
+
+
+# logger = get_logger(__name__)
+
+
+# class HybridLouvain:
+#     def __init__(
+#         self,
+#         G: nx.Graph,
+#         B: pd.DataFrame,
+#         Y,
+#         k3: float = 0.2,
+#         k4: float = 0.8,
+#         max_iter: int = 10,
+#         weight: str = "weight",
+#         tune: bool = False,
+#     ):
+#         self.logger = get_logger(__name__)
+#         self.G = G
+#         self.B = B
+#         self.Y = Y
+#         self.k3 = k3
+#         self.k4 = k4
+#         self.weight = weight
+#         self.max_iter = max_iter
+#         self.tune = tune
+#         self.logger.info(
+#             f"Initialized HybridLouvain with max_iter={self.max_iter}, k3={self.k3}, k4={self.k4}"
+#         )
+
+#     def run(self) -> dict:
+#         iteration = 0
+#         prev_size = len(self.G.nodes())
+#         current_partition = None
+
+#         all_clusters = {}
+
+#         while iteration < self.max_iter:
+#             self.logger.info(
+#                 f"\nIteration {iteration+1}/{self.max_iter}: Running Correlated Louvain..."
+#             )
+#             clouvain = CorrelatedLouvain(
+#                 self.G,
+#                 B=self.B,
+#                 Y=self.Y,
+#                 k3=self.k3,
+#                 k4=self.k4,
+#                 weight=self.weight,
+#                 tune=self.tune,
+#             )
+#             partition = clouvain.run()
+#             quality_val = clouvain.get_quality()
+
+#             self.logger.info(f"Iteration {iteration+1}: Quality = {quality_val:.4f}")
+#             current_partition = partition
+
+#             best_corr = 0
+#             best_seed = None
+#             # loop over communities in the partition to find the best seed community.
+#             for com in set(partition.values()):
+#                 nodes = [n for n in self.G.nodes() if partition[n] == com]
+#                 if len(nodes) < 2:
+#                     continue
+#                 try:
+#                     corr, _ = clouvain._compute_community_correlation(nodes)
+#                     if abs(corr) > abs(best_corr):
+#                         best_corr = corr
+#                         best_seed = nodes
+#                 except Exception as e:
+#                     self.logger.info(
+#                         f"Error computing correlation for community {com}: {e}"
+#                     )
+#             if best_seed is None:
+#                 self.logger.info("No valid seed community found; stopping iterations.")
+#                 break
+#             self.logger.info(
+#                 f"Selected seed community of size {len(best_seed)} with correlation {best_corr:.4f}"
+#             )
+
+#             self.logger.info(
+#                 "Running Correlated PageRank on selected seed community..."
+#             )
+#             pagerank_instance = CorrelatedPageRank(
+#                 graph=self.G,
+#                 omics_data=self.B,
+#                 phenotype_data=self.Y,
+#                 tune=self.tune,
+#             )
+#             pagerank_results = pagerank_instance.run(best_seed)
+#             refined_nodes = pagerank_results.get("cluster_nodes", [])
+
+#             new_size = len(refined_nodes)
+#             all_clusters[iteration] = refined_nodes
+#             self.logger.info(f"Refined subgraph size: {new_size}")
+#             if new_size == prev_size:
+#                 self.logger.info("Subgraph size converged. Stopping iterations.")
+#                 break
+#             elif new_size <= 1:
+#                 self.logger.info(
+#                     "Refined subgraph contains only one node. Stopping iterations."
+#                 )
+#                 break
+#             prev_size = new_size
+#             self.G = self.G.subgraph(refined_nodes).copy()
+#             iteration += 1
+
+#         self.logger.info(f"Hybrid Louvain completed after {iteration+1} iterations.")
+#         return {"curr": current_partition, "clus": all_clusters}

From d559dd6ee3e0f2d558b53169b51c901a8be01442 Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 6 Feb 2025 07:46:46 -0700
Subject: [PATCH 24/26] Merge changes from downstreams branches: final review
 before merging to main

---
 ...2016__BI__Gene__Firehose_RPKM_log2.cct.csv | 824 ++++++++++++++++++
 ...1_28_2016__BI__Gene__Firehose_RPPA.cct.csv | 176 ++++
 ...01_28_2016__BI__Clinical__Firehose.tsi.csv |  21 +
 bioneuralnet/external_tools/SmCCNet.R         |  64 +-
 bioneuralnet/external_tools/smccnet.py        | 189 ++--
 bioneuralnet/utils/__init__.py                |   6 +-
 bioneuralnet/utils/convert_Rdata_to_csv.R     |  24 +
 bioneuralnet/utils/rdata_to_csv.py            |  54 ++
 8 files changed, 1221 insertions(+), 137 deletions(-)
 create mode 100644 bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv
 create mode 100644 bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv
 create mode 100644 bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv
 create mode 100644 bioneuralnet/utils/convert_Rdata_to_csv.R
 create mode 100644 bioneuralnet/utils/rdata_to_csv.py

diff --git a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv
new file mode 100644
index 0000000..3e7289a
--- /dev/null
+++ b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv
@@ -0,0 +1,824 @@
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diff --git a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv
new file mode 100644
index 0000000..c6a0e6b
--- /dev/null
+++ b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv
@@ -0,0 +1,176 @@
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diff --git a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv
new file mode 100644
index 0000000..07396ab
--- /dev/null
+++ b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv
@@ -0,0 +1,21 @@
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atinglobularcarcinoma,infiltratinglobularcarcinoma,infiltratinglobularcarcinoma,infiltratinglobularcarcinoma
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diff --git a/bioneuralnet/external_tools/SmCCNet.R b/bioneuralnet/external_tools/SmCCNet.R
index d0d9b39..7f5493c 100644
--- a/bioneuralnet/external_tools/SmCCNet.R
+++ b/bioneuralnet/external_tools/SmCCNet.R
@@ -8,7 +8,7 @@ library("dplyr")
 options(stringsAsFactors = FALSE)
 allowWGCNAThreads()
 
-
+# Read JSON input from stdin
 json_input <- readLines(con = "stdin")
 if (length(json_input) == 0) {
   stop("No input data received.")
@@ -26,16 +26,48 @@ if (!("SampleID" %in% colnames(phenotype_df))) {
 rownames(phenotype_df) <- phenotype_df$SampleID
 
 omics_keys <- grep("^omics_", names(input_data), value = TRUE)
-if (length(omics_keys) == 0) {
+if (length(omics_keys) < 1) {
   stop("No omics data found in input.")
 }
-omics_df <- read.csv(text = input_data[[omics_keys[1]]], stringsAsFactors = FALSE)
-if (!("SampleID" %in% colnames(omics_df))) {
-  stop("SampleID column not found in omics data.")
+
+# Initialize list to store each omics matrix
+omics_list <- list()
+
+# Loop over all omics keys
+for (key in omics_keys) {
+  omics_df <- read.csv(text = input_data[[key]], stringsAsFactors = FALSE)
+  if (!("SampleID" %in% colnames(omics_df))) {
+    stop(paste("SampleID column not found in", key))
+  }
+  rownames(omics_df) <- omics_df$SampleID
+  # Convert to matrix: assuming the first column are the feature names
+  omics_values <- as.matrix(omics_df[, -1])
+  # Standardize features
+  omics_values <- scale(omics_values)
+  # Subset to common samples with phenotype
+  common_samples <- intersect(rownames(omics_values), rownames(phenotype_df))
+  if (length(common_samples) == 0) {
+    stop(paste("No matching sample IDs between", key, "and phenotype data."))
+  }
+  omics_values <- omics_values[common_samples, , drop = FALSE]
+  # Store in list
+  omics_list[[length(omics_list) + 1]] <- omics_values
+}
+
+# Also subset phenotype data to common samples across all omics.
+# in here we are assuming that all omics datasets share the same samples.
+common_samples_all <- rownames(phenotype_df)
+for (mat in omics_list) {
+  common_samples_all <- intersect(common_samples_all, rownames(mat))
+}
+if (length(common_samples_all) == 0) {
+  stop("No common samples across all omics datasets and phenotype data.")
 }
-rownames(omics_df) <- omics_df$SampleID
-omics_values <- as.matrix(omics_df[, -1])
+phenotype_df <- phenotype_df[common_samples_all, , drop = FALSE]
+# Also, subset each omics matrix in the list
+omics_list <- lapply(omics_list, function(mat) mat[common_samples_all, , drop = FALSE])
 
+# Parse command-line arguments
 args <- commandArgs(trailingOnly = TRUE)
 if (length(args) != 4) {
   stop("Exactly 4 arguments must be supplied: data_types, kfold, summarization, seed")
@@ -53,14 +85,12 @@ if (!(summarization %in% c("PCA", "SVD", "NetSHy"))) {
 }
 set.seed(seed)
 
-
-common_samples <- intersect(rownames(omics_values), rownames(phenotype_df))
-if (length(common_samples) == 0) {
-  stop("No matching sample IDs between omics data and phenotype.")
+# Ensure that the number of data_types matches the number of omics datasets
+if (length(data_types) != length(omics_list)) {
+  stop("Number of omics datasets does not match number of data types provided.")
 }
-omics_values <- omics_values[common_samples, , drop = FALSE]
-phenotype_df <- phenotype_df[common_samples, , drop = FALSE]
 
+# Extract phenotype vector (assume phenotype is in second column) since R is 1-indexed
 Y <- as.numeric(phenotype_df[[2]])
 if (any(is.na(Y))) {
   stop("Phenotype vector contains NA values.")
@@ -87,7 +117,7 @@ tryCatch(
 
     if (!is.null(ncomp_pls)) {
       result <- fastAutoSmCCNet(
-        X = list(omics_values),
+        X = omics_list,
         Y = Y,
         DataType = data_types,
         Kfold = kfold,
@@ -99,7 +129,7 @@ tryCatch(
       )
     } else {
       result <- fastAutoSmCCNet(
-        X = list(omics_values),
+        X = omics_list,
         Y = Y,
         DataType = data_types,
         Kfold = kfold,
@@ -115,8 +145,6 @@ tryCatch(
     quit(status = 1)
   }
 )
-
+# Write output to a csv
 write.csv(result$AdjacencyMatrix, file = "AdjacencyMatrix.csv", row.names = TRUE)
-
-
 quit(status = 0)
diff --git a/bioneuralnet/external_tools/smccnet.py b/bioneuralnet/external_tools/smccnet.py
index dee7b6d..83084aa 100644
--- a/bioneuralnet/external_tools/smccnet.py
+++ b/bioneuralnet/external_tools/smccnet.py
@@ -1,10 +1,12 @@
 import os
 import subprocess
 import pandas as pd
+from pathlib import Path
+import json
+import tempfile
 from typing import List, Dict, Any
 from ..utils.logger import get_logger
-import json
-from pathlib import Path
+import shutil
 
 
 class SmCCNet:
@@ -12,7 +14,7 @@ class SmCCNet:
     SmCCNet Class for Graph Generation using Sparse Multiple Canonical Correlation Networks (SmCCNet).
 
     This class handles the preprocessing of omics data, execution of the SmCCNet R script,
-    and retrieval of the resulting adjacency matrix, all using in-memory data structures.
+    and retrieval of the resulting adjacency matrix from a designated output directory.
     """
 
     def __init__(
@@ -21,19 +23,21 @@ def __init__(
         omics_dfs: List[pd.DataFrame],
         data_types: List[str],
         kfold: int = 5,
-        summarization: str = "PCA",
+        summarization: str = "NetSHy",
         seed: int = 732,
+        output_dir: str = None,
     ):
         """
         Initializes the SmCCNet instance.
 
         Args:
-            phenotype_df (pd.DataFrame): DataFrame containing phenotype data. The first column should be sample IDs.
-            omics_dfs (List[pd.DataFrame]): List of DataFrames, each representing an omics dataset. Each DataFrame should have sample IDs as the first column.
+            phenotype_df (pd.DataFrame): DataFrame containing phenotype data.
+            omics_dfs (List[pd.DataFrame]): List of omics DataFrames.
             data_types (List[str]): List of omics data types (e.g., ["protein", "metabolite"]).
             kfold (int, optional): Number of folds for cross-validation. Defaults to 5.
-            summarization (str, optional): Summarization method. Defaults to "PCA".
-            seed (int, optional): Random seed for reproducibility. Defaults to 732.
+            summarization (str, optional): Summarization method. Defaults to "NetSHy".
+            seed (int, optional): Random seed. Defaults to 732.
+            output_dir (str, optional): Folder to write temporary files. If None, a temporary directory is created.
         """
         self.phenotype_df = phenotype_df
         self.omics_dfs = omics_dfs
@@ -43,111 +47,106 @@ def __init__(
         self.seed = seed
 
         self.logger = get_logger(__name__)
-        self.logger.info("Initialized SmCCNet with the following parameters:")
+        self.logger.info("Initialized SmCCNet with parameters:")
         self.logger.info(f"K-Fold: {self.kfold}")
         self.logger.info(f"Summarization: {self.summarization}")
         self.logger.info(f"Seed: {self.seed}")
 
         if len(self.omics_dfs) != len(self.data_types):
             self.logger.error(
-                "Number of omics dataframes does not match number of data types."
+                "Number of omics DataFrames does not match number of data types."
             )
-            raise ValueError(
-                "Number of omics dataframes does not match number of data types."
+            raise ValueError("Mismatch between omics dataframes and data types.")
+
+        if output_dir is None:
+            # Use TemporaryDirectory context manager for automatic cleanup
+            self.temp_dir_obj = tempfile.TemporaryDirectory()
+            self.output_dir = self.temp_dir_obj.name
+            self.logger.info(
+                f"No output_dir provided; using temporary directory: {self.output_dir}"
             )
+        else:
+            self.output_dir = output_dir
+            os.makedirs(self.output_dir, exist_ok=True)
 
     def preprocess_data(self) -> Dict[str, Any]:
         """
-        Preprocesses (lightly validates) the phenotype and omics data:
-        - Includes sample IDs as a separate column.
-        - Checks that each omics DataFrame has the same number of samples as the phenotype DataFrame.
-        - Finds and retains common sample IDs between phenotype and each omics DataFrame.
-        - Serializes the phenotype and each omics DataFrame to CSV.
+        Preprocess the phenotype and omics data:
+         - Reset indexes, standardize sample IDs, and serialize to CSV.
 
         Returns:
-            Dict[str, Any]: Dictionary containing serialized phenotype and omics data.
-                            Keys:
-                            "phenotype" : CSV string of the phenotype DataFrame.
-                            "omics_1", "omics_2", ...: CSV strings of each omics DataFrame.
+            Dict[str, Any]: A dictionary with keys 'phenotype', 'omics_1', etc.
         """
-        self.logger.info("Validating phenotype and omics data...")
+        self.logger.info("Validating and serializing input data for SmCCNet...")
         pheno_df = (
             self.phenotype_df.copy().reset_index().rename(columns={"index": "SampleID"})
         )
-        num_samples = len(pheno_df)
-        pheno_id_col = "SampleID"
-        self.logger.info(f"Number of samples in phenotype data: {num_samples}")
-
-        pheno_df[pheno_id_col] = (
-            pheno_df[pheno_id_col].astype(str).str.strip().str.upper()
-        )
-
+        pheno_df["SampleID"] = pheno_df["SampleID"].astype(str).str.strip().str.upper()
         serialized_data = {"phenotype": pheno_df.to_csv(index=False)}
 
         for i, omics_df in enumerate(self.omics_dfs, start=1):
-            current_key = f"omics_{i}"
+            key = f"omics_{i}"
             df = omics_df.copy().reset_index().rename(columns={"index": "SampleID"})
-
-            omics_id_col = "SampleID"
-            df[omics_id_col] = df[omics_id_col].astype(str).str.strip().str.upper()
-
-            common_ids = set(pheno_df[pheno_id_col]).intersection(set(df[omics_id_col]))
-            self.logger.info(
-                f"Number of common samples in {current_key}: {len(common_ids)}"
-            )
-
-            if len(common_ids) == 0:
+            df["SampleID"] = df["SampleID"].astype(str).str.strip().str.upper()
+            # Intersect sample IDs between phenotype and omics data
+            common_ids = set(pheno_df["SampleID"]).intersection(set(df["SampleID"]))
+            if not common_ids:
                 raise ValueError(
-                    f"No overlapping sample IDs between phenotype and {current_key}."
+                    f"No overlapping sample IDs between phenotype and {key}."
                 )
-
-            pheno_df = pheno_df[pheno_df[pheno_id_col].isin(common_ids)]
-            df = df[df[omics_id_col].isin(common_ids)]
-            df = df.set_index(omics_id_col).loc[pheno_df[pheno_id_col]].reset_index()
-            serialized_data[current_key] = df.to_csv(index=False)
-
-        self.logger.info("Preprocessing checks completed successfully.")
+            df = df[df["SampleID"].isin(common_ids)]
+            # Ensure ordering follows phenotype data
+            df = df.set_index("SampleID").loc[pheno_df["SampleID"]].reset_index()
+            serialized_data[key] = df.to_csv(index=False)
+            self.logger.info(f"Serialized {key} with {len(df)} samples.")
         return serialized_data
 
     def run_smccnet(self, serialized_data: Dict[str, Any]) -> None:
         """
-        Executes the SmCCNet R script by passing serialized data via standard input.
+        Executes the SmCCNet R script in the specified output directory.
 
         Args:
-            serialized_data (Dict[str, Any]): Dictionary containing serialized phenotype and omics data.
-
-        Returns:
-            str: Serialized adjacency matrix JSON string from R script.
+            serialized_data (Dict[str, Any]): Serialized CSV strings for phenotype and omics data.
         """
-
         try:
-            self.logger.info("Preparing data for SmCCNet R script.")
+            self.logger.info("Executing SmCCNet R script...")
             json_data = json.dumps(serialized_data) + "\n"
-
             script_dir = os.path.dirname(os.path.abspath(__file__))
-            r_script = os.path.join(script_dir, "SmCCNet.R")
 
+            r_script = os.path.join(script_dir, "SmCCNet.R")
             if not os.path.isfile(r_script):
                 self.logger.error(f"R script not found: {r_script}")
                 raise FileNotFoundError(f"R script not found: {r_script}")
 
+            # Dynamically locate Rscript in the system path
+            rscript_path = shutil.which("Rscript")
+            if rscript_path is None:
+                raise EnvironmentError("Rscript not found in system PATH.")
+
             command = [
-                "Rscript",
+                rscript_path,
                 r_script,
                 ",".join(self.data_types),
                 str(self.kfold),
                 self.summarization,
                 str(self.seed),
             ]
-
-            self.logger.debug(f"Executing command: {' '.join(command)}")
-
-            subprocess.run(
-                command, input=json_data, text=True, capture_output=True, check=True
+            self.logger.debug(
+                f"Running command: {' '.join(command)} in cwd={self.output_dir}"
             )
-
-            self.logger.info("SmCCNet R script executed successfully.")
-
+            result = subprocess.run(
+                command,
+                input=json_data,
+                text=True,
+                capture_output=True,
+                check=True,
+                cwd=self.output_dir,
+            )
+            self.logger.info(f"SMCCNET R script output:\n{result.stdout}")
+            if result.stderr:
+                self.logger.warning(
+                    f"SMCCNET R script warnings/errors:\n{result.stderr}"
+                )
         except subprocess.CalledProcessError as e:
             self.logger.error(f"R script execution failed: {e.stderr}")
             raise
@@ -157,60 +156,20 @@ def run_smccnet(self, serialized_data: Dict[str, Any]) -> None:
 
     def run(self) -> pd.DataFrame:
         """
-        Executes the entire Sparse Multiple Canonical Correlation Network (SmCCNet) workflow.
-
-        **Steps:**
-
-        1. **Preprocessing Data**:
-           - Formats and serializes the input omics and phenotype data for SmCCNet analysis.
-
-        2. **Graph Generation**:
-           - Constructs a global network by generating an adjacency matrix through SmCCNet.
-
-        3. **Postprocessing Results**:
-           - Deserializes the adjacency matrix (output of SmCCNet) into a Pandas DataFrame.
-
-        **Returns**: pd.DataFrame
-
-            - A DataFrame containing the adjacency matrix, where each entry represents the strength of the correlation between features.
-
-        **Raises**:
-
-            - **ValueError**: If the input data is improperly formatted or missing.
-            - **Exception**: For any unforeseen errors encountered during preprocessing, graph generation, or postprocessing.
+        Runs the full SmCCNet workflow and returns the generated adjacency matrix.
 
-        **Notes**:
-
-            - SmCCNet is designed for multi-omics data and requires a well-preprocessed and normalized dataset.
-            - Ensure that omics and phenotype data are properly aligned to avoid errors in graph construction.
-
-        **Example**:
-
-        .. code-block:: python
-
-            smccnet = SmCCNet(omics_data, phenotype_data)
-            adjacency_matrix = smccnet.run()
-            print(adjacency_matrix.head())
+        Returns:
+            pd.DataFrame: The adjacency matrix.
         """
         try:
-            self.logger.info("Starting SmCCNet Graph Generation Workflow.")
+            self.logger.info("Starting SmCCNet workflow.")
             serialized_data = self.preprocess_data()
             self.run_smccnet(serialized_data)
-
-            self.logger.info("Loading adjacency matrix from CSV.")
-            adjacency_path = Path.cwd() / "adjacencyMatrix.csv"
-            self.logger.debug(f"Adjacency matrix path: {adjacency_path}")
-
+            adjacency_path = Path(self.output_dir) / "AdjacencyMatrix.csv"
+            self.logger.info(f"Reading adjacency matrix from: {adjacency_path}")
             adjacency_df = pd.read_csv(adjacency_path, index_col=0)
-            self.logger.info(
-                "Adjacency matrix loaded with shape: %s", adjacency_df.shape
-            )
-
-            self.logger.info(
-                "Adjacency matrix loaded with shape: %s", adjacency_df.shape
-            )
-            self.logger.info("SmCCNet Graph Generation completed successfully.")
+            self.logger.info(f"Adjacency matrix shape: {adjacency_df.shape}")
             return adjacency_df
         except Exception as e:
-            self.logger.error(f"Error in SmCCNet Graph Generation: {e}")
+            self.logger.error(f"Error in SmCCNet workflow: {e}")
             raise
diff --git a/bioneuralnet/utils/__init__.py b/bioneuralnet/utils/__init__.py
index b83642f..459c9c5 100644
--- a/bioneuralnet/utils/__init__.py
+++ b/bioneuralnet/utils/__init__.py
@@ -1,7 +1,5 @@
 from .logger import get_logger
 from .path_utils import validate_paths
+from .rdata_to_csv import convert_rdata_to_csv
 
-__all__ = [
-    "get_logger",
-    "validate_paths",
-]
+__all__ = ["get_logger", "validate_paths", "convert_rdata_to_csv"]
diff --git a/bioneuralnet/utils/convert_Rdata_to_csv.R b/bioneuralnet/utils/convert_Rdata_to_csv.R
new file mode 100644
index 0000000..d5784fb
--- /dev/null
+++ b/bioneuralnet/utils/convert_Rdata_to_csv.R
@@ -0,0 +1,24 @@
+#!/usr/bin/env Rscript
+
+# check for arguments
+args <- commandArgs(trailingOnly = TRUE)
+if (length(args) != 2) {
+    stop("Usage: convert_Rdata_to_csv.R input.Rdata output.csv", call. = FALSE)
+}
+
+input_file <- args[1]
+output_file <- args[2]
+
+# load the Rdata file to the workspace
+load(input_file)
+
+if (exists("AdjacencyMatrix")) {
+    mat <- AdjacencyMatrix
+} else if (exists("M")) {
+    mat <- M
+} else {
+    stop("Neither 'AdjacencyMatrix' nor 'M' was found in the Rdata file.", call. = FALSE)
+}
+
+# write the matrix or data.frame to a csv file.
+write.csv(mat, file = output_file, row.names = TRUE, quote = FALSE)
diff --git a/bioneuralnet/utils/rdata_to_csv.py b/bioneuralnet/utils/rdata_to_csv.py
new file mode 100644
index 0000000..072a7d1
--- /dev/null
+++ b/bioneuralnet/utils/rdata_to_csv.py
@@ -0,0 +1,54 @@
+import subprocess
+import shutil
+from pathlib import Path
+from logger import get_logger
+
+
+def convert_rdata_to_csv(rdata_file: Path, csv_file: Path) -> None:
+    """
+    Convert an .Rdata file to CSV by invoking the convert_Rdata_to_csv.R script using Rscript.
+
+    This function assumes that:
+      - The R script 'convert_Rdata_to_csv.R' is in the same directory as this module.
+      - R is installed and Rscript is available in the system PATH.
+      - The RData file contains an object named either "AdjacencyMatrix" or "M".
+
+    Args:
+        rdata_file (Path): Path to the input .Rdata file.
+        csv_file (Path): Path to the output CSV file.
+
+    Raises:
+        EnvironmentError: If Rscript is not found.
+        FileNotFoundError: If the R script is not found.
+        Exception: If the R script fails to execute.
+    """
+    logger = get_logger(__name__)
+    rscript_path = shutil.which("Rscript")
+    if rscript_path is None:
+        raise EnvironmentError("Rscript not found in system PATH.")
+
+    script_path = Path(__file__).parent / "convert_Rdata_to_csv.R"
+    if not script_path.exists():
+        raise FileNotFoundError(f"R script not found: {script_path}")
+
+    command = [rscript_path, str(script_path), str(rdata_file), str(csv_file)]
+    logger.info(f"Running command: {' '.join(command)}")
+
+    result = subprocess.run(command, capture_output=True, text=True)
+
+    if result.returncode != 0:
+        logger.info("Error executing R script:")
+        logger.info(result.stderr)
+        raise Exception("R script execution failed.")
+    else:
+        logger.info(result.stdout)
+        logger.info(f"CSV file saved to: {csv_file}")
+
+
+# Example usage:
+if __name__ == "__main__":
+    # Replace with your actual paths
+    rdata_path = Path("path/to/your_file.Rdata")
+    csv_output_path = Path("path/to/your_file.csv")
+
+    convert_rdata_to_csv(rdata_path, csv_output_path)

From 189771f8aef3abe13118bf65315f5544a38a627a Mon Sep 17 00:00:00 2001
From: ramosv <56368263+ramosv@users.noreply.github.com>
Date: Thu, 6 Feb 2025 07:58:34 -0700
Subject: [PATCH 25/26] Merge changes from downstreams branches: final review
 before merging to main: Manual check

---
 ...2016__BI__Gene__Firehose_RPKM_log2.cct.csv | 824 ------------------
 ...1_28_2016__BI__Gene__Firehose_RPPA.cct.csv | 176 ----
 ...01_28_2016__BI__Clinical__Firehose.tsi.csv |  21 -
 3 files changed, 1021 deletions(-)
 delete mode 100644 bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv
 delete mode 100644 bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv
 delete mode 100644 bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv

diff --git a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv
deleted file mode 100644
index 3e7289a..0000000
--- a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__BDGSC__miRNASeq__HS_miR__01_28_2016__BI__Gene__Firehose_RPKM_log2.cct.csv
+++ /dev/null
@@ -1,824 +0,0 @@
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-hsa-mir-18b,1.2093,1.6064,0.286,0.707,0,0.1848,0.3065,0.3506,1.6279,0.3144,0.596,0,1.162,1.2491,0,0,1.5258,0.6285,0.2004,0.6738,0,0.5094,0,1.4663,0.3262,0.4673,1.4128,0,0,1.2704,0,0.3243,0.715,0.365,0,0.4673,0,0,0,0.7959,1.5205,1.0641,0,0,0,0.4882,0.2498,2.7387,1.0019,1.669,1.816,0.28,1.9858,0.2568,0.9185,1.283,0,0.7622,0.4735,0.3137,0.3847,0,0.5157,0.352,0.1837,0.2388,0.6421,0,0.2938,0.1768,0.097,0.7538,0,0,0.8504,0.3773,1.3479,1.4473,0,0.6878,0.2333,0.8861,0.2511,0.5614,0,0,1.1401,0,0.7272,0,0,0.8291,0.2634,0.3687,0,0,0.373,0.361,0.6552,0.9032,0.3599,1.2314,0.4832,5.391,0.3699,0.2859,0.4993,0.5198,0.221,0,0.4696,0,0.8023,0.3079,1.004,0.3291,0,0.1522,0.7762,0.5009,1.0711,0.4632,0.7329,2.0488,1.3534,0.8594,0.9462,0,0.1703,0,0.5169,0.8082,0.2445,0,0.2494,0.2016,0.1694,0,0.3495,0.8141,0.163,0.4279,0.1956,0,0.6347,0.308,1.5138,0.4125,0.3365,0,0,2.5464,0.6446,0,0.5023,0,2.6091,0.8952,0.4204,0.7219,0,0.4592,0,0,0,1.4829,0,0,2.792,0,0,0.3733,0.1931,0.9532,0.9228,0,2.4746,1.7795,1.0725,1.3359,0.2809,0,0,0,1.3229,0.478,0,0,0.4924,0.4153,0,0,0,0.418,0.5413,0.5931,0.4417,0,0,0.4662,1.7795,0.7862,1.6594,0.6559,2.0286,0.3557,0.5646,0.6949,0.845,0.3837,0.6331,0.3145,0,0.1627,0.3436,0.1392,2.2198,0,0.1534,0.1678,0,0.953,0.4783,0.8967,0.8215,0.4611,1.0518,0,0,0,0.5937,1.1342,1.1323,1.4392,0.2407,1.8265,0.2436,0.5308,0.9763,0.8771,0,0.8193,0,0.4933,1.2897,0.8463,0.874,0,0.3758,0,0,0.793,0.8444,0,0.8085,0,1.4583,0,0.7938,2.3103,0.9803,1.4542,0.3014,0,0.6834,0.6428,0.6568,0.3675,0,0.6206,0,0.3922,0,0,0.3513,0.3836,0,0,0.719,0.7946,0,0,0,0,0,2.2101,0,0,0.5584,0,0,0,0.3877,0,0,0,0,0.821,0.6082,0.5827,0.5582,0,0,0.8139,0.8177,1.6185,1.6621,0,0.3621,0,0,2.6491,0.7952,0,0,0,0.4178,0.7069,1.8952,0,0,0.1657,1.1533,0.3202,0.2513,0.6083,0.6011,0.1325,0.1874,0,0,0,0.1525,0.9051,0,0.319,0,0,0.1461,0.0784,0.4273,1.9592,1.1004,0.6796,0,0,1.279,0.6289,0.4366,0.7552,1.8036,0,0.5342,0,0.8741,0,2.1749,1.0798,0,1.4408,1.6865,0,1.2681,1.1127,1.8096,0.6271,1.2702,2.875,2.323,0,2.8487,0.4632,1.5661,0,0.5085,0,1.0136,0,0.6706,0,1.1303,1.1278,1.642,1.7494,0.5263,0.757,0,0.6343,0.6885,0,2.8516,1.3258,0.3468,0.407,0,3.0298,0,0,0.742,2.2461,0,1.0873,0.4443,0,0,0.3669,0.6165,1.8909,0.3434,0,0.8182,0,0.6214,0,0,0.9781,0,0,1.7808,0.8546,0,0,0,0.5007,0.4109,0.3389,0.4664,0.9082,0,0.84,1.0903,0,0,0.9743,0.461,1.5275,0.2641,0.3858,0,0,0.925,0.7386,0,0.9702,0.9201,0,1.1516,0.2747,0.7028,0,0.6052,0.2515,0.7444,0,0,0.8072,0.4191,0,0.7009,0.3259,0,0.5842,0.2582,0,0.3827,0.1096,0.126,0.3759,1.9099,1.1737,0.3795,0,1.2412,0,0.775,0.7908,0.6713,0.421,1.2693,1.1253,0.8756,0.707,1.0662,0.5256,0,0,0,1.2618,1.7993,0,0.8572,0.6052,0,0.5064,0.4249,1.0084,1.0042,2.0776,0.611,0.6851,0.5826,0,0,0,1.4044,1.0036,0.5573,0.5755,0.3303,0,0.992,2.0259,0.6385,0.7227,0.3376,0,0.6031,0.3093,1.9127,1.3062,0.257,0.1936,0,0.2834,0.4939,0,2.1005,1.1323,0.8978,0,0.3776,1.037,0,0.543,2.3954,0.8236,0.9391,1.3765,0.7403,0.6039,0.3842,1.385,0.1206,0.4253,0.4867,0,0.2431,1.067,0.4078,0.4906,0,1.0916,0,0.5528,0,0.5773,0.6674,0,0.3019,0.7092,1.0034,0.6599,0,0.4953,0,1.0473,0.6501,0,0.2975,1.2553,0.8833,0.7338,0,1.2358,0.4536,0,0,0,0,0.7007,1.0132,0,0,0,0.7251,0.1718,2.6548,0.4417,1.4802,1.3293,0,0.7352,2.0593,0.4404,0,0.6188,0,0.7295,0.2573,1.6902,0.2634,0.6583,2.3486,0.7845,1.1705,1.1696,1.1582,0.2198,0,0,0,0.5934,0,0,0.9765,0,0.8889,1.3391,0.3662,0.2022,0.3479,0.9092,0,0.3315,0,1.8687,0.9388,0.5378,0.8944,0.9631,0.3977,1.1668,0,0.6959,0,0,0.6305,0,0,0.2552,0.5826,0.7858,0,1.0757,0.4471,0,0,0.7676,1.4141,0,0.1877,0,0.5768,0.4158,0.8309,1.7685,0.5553,0.9101,1.5984,0.3671,0.9736,0.5259,0.2122,1.597,0.4345,0.6897,0.8806,1.5029,0,2.3138,0.5615,0,0.4281,0.1885,2.1255,0.2468,0.6334,1.2954,0.1898,0.6773,0,0.3643,0,0,0.7786,0.7757,0.5754,0.798,0.139,1.1603,0.7668,0.5566,0.6727,0.816,0,0.7168,0.2996,0.2912,0.2051,0.2178,0,0,0,0,0.4827,0,1.7313,0.3667,0,0,0,0.6624,0.8086,0.3997,1.3356,0.3247,0.2381,1.3322,0,0,0,0.2149,0,0.3735,2.385,0.4529,0,0,0.398,1.5744,1.0229,0.4011,1.0797,0.3432,1.0328,0.2511,0.8425,0,0,0,0.2224,0.2794,0.1955,1.3959,0.204,0.35,0.5093,0,0.5459,0,0.4914,0.1385,1.6941,0.1866,1.321
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-hsa-mir-642a,1.5234,3.0693,2.7207,1.6842,0.5719,0.9685,1.8013,2.6386,1.4182,1.7804,2.1546,2.1621,3.1438,2.3592,5.8222,0.4616,1.0893,3.5753,2.1941,2.5993,1.7853,1.987,2.5979,3.0864,2.1038,2.9792,2.145,3.3664,1.9118,4.0474,0,3.22,0.715,3.3109,1.9468,1.8787,2.6905,3.0593,1.9787,0.1982,3.6104,4.9286,0.7424,5.0566,4.9834,1.1427,0.6481,1.2534,1.5875,1.3978,0.88,0.8928,1.7522,1.1169,2.5377,0.5635,0.2785,1.0315,0.4735,2.7719,1.7845,1.9654,2.4051,1.2519,1.5364,0.9261,0.6421,2.8485,3.7223,1.3587,1.9457,1.2464,1.2571,0.5939,1.6997,2.718,1.6138,2.3631,0.4895,4.0243,0.909,2.3302,1.2207,0.9645,1.8091,1.3149,0.8953,1.3629,1.5686,2.4691,0.8371,0.8291,0.849,0.3687,0.5216,0.3784,0.7971,0.6495,2.3829,3.8381,1.2729,3.8825,2.2867,2.0534,1.2995,1.2778,0.7855,1.1631,3.0647,0.4239,0.8233,1.561,1.4189,0.9048,2.6848,0.1739,3.4337,0.1522,0.6177,2.0547,1.449,1.8975,2.9708,1.8115,1.2462,2.9857,1.0421,1.0504,2.9335,1.6355,2.4083,1.5712,2.5818,1.3776,0.2494,2.1044,1.085,1.2615,1.0679,2.2603,3.114,2.3628,1.5299,2.7972,1.6827,1.0502,1.1616,2.6919,4.4778,3.1313,2.9147,1.5542,3.0893,2.2225,1.8068,3.1689,2.6091,1.4435,1.2347,0,4.0818,1.5231,0,2.5264,0.4492,3.134,3.16,0,3.1075,3.0978,1.7274,2.9621,1.2821,3.0038,3.1066,3.7425,3.7827,1.9708,4.3039,3.6182,2.2186,2.2674,3.677,2.0843,1.0008,2.9033,0.3149,1.6537,2.8688,1.2231,2.8764,1.6798,2.8228,3.3327,1.4964,0.3268,2.9646,2.2522,2.4241,1.7175,2.7738,1.2922,2.4109,3.9707,3.4036,1.631,0.9693,2.3283,0,0.8169,0.351,0.3145,0.299,0.1627,0.4181,0,0,0.9102,1.362,1.0771,2.6782,0.953,1.3632,1.5341,0,2.6049,2.8951,0.816,0,3.1563,1.8261,0,3.3134,1.3343,1.5836,0.8867,1.9792,1.2229,2.2103,1.3254,2.3221,0,3.4466,2.223,2.7089,1.4623,4.0723,3.0977,1.6244,1.553,2.2167,2.6155,1.6422,1.4252,2.5258,1.5388,2.7711,1.7673,3.3974,1.7061,3.2375,2.0162,4.3091,2.9331,1.5944,1.086,1.8459,3.0947,2.3338,2.079,3.6455,1.4426,2.8666,2.9749,1.7996,2.9451,3.2176,4.4213,2.3235,2.1468,2.9656,0,4.0249,2.2175,1.6547,3.0451,3.786,2.2085,2.3934,4.0825,2.1418,2.7076,2.0297,0,3.1992,3.464,5.7186,0.821,3.4315,2.3166,2.2569,2.5581,2.3047,2.0114,2.6051,3.318,3.5636,1.4611,2.1451,4.1854,1.7919,3.1552,1.6807,1.5393,1.0783,3.6825,1.2288,4.071,2.2653,3.076,2.0695,1.6739,2.4565,0.5821,3.5193,1.8358,4.0223,1.2889,1.1687,0,2.135,1.1039,1.2925,1.2073,0.9357,0.9927,1.0013,2.1811,0.2787,0.5328,1.4455,2.5396,2.8283,1.1397,2.1277,2.8245,0,1.7902,1.6418,0.7552,2.7235,1.2386,3.4991,3.6748,3.345,0.3656,2.5528,3.6013,2.1506,2.6192,2.2641,4.2308,3.8694,3.4693,4.3953,3.014,2.5709,2.2081,2.4458,4.4056,4.1434,4.6353,4.4104,2.6553,1.4832,2.3719,1.185,2.8185,1.31,2.8504,3.101,3.7616,2.5726,1.125,0.6663,0.9411,2.4817,2.1087,0.2677,3.5458,4.2967,1.7701,2.7456,2.1971,2.3484,1.5979,0.8568,1.491,2.6736,4.3262,3.3888,4.4091,1.058,1.7611,0.909,4.2223,2.7792,2.6038,1.9842,3.5492,1.1839,1.8573,1.2302,2.7336,1.208,3.5841,3.456,1.2362,2.3392,3.5245,4.332,0.5084,0.2785,3.454,1.8623,3.4328,2.1488,4.212,2.9237,0,2.4638,0,0,2.8621,3.1242,4.5983,1.3823,2.9533,2.5494,2.3044,3.5217,2.5059,4.2279,2.4531,4.0926,0,1.3375,2.1907,0.2741,1.9486,0.6052,0,2.3368,0.6182,2.0546,0.9998,2.235,2.6572,1.0304,3.6182,1.3623,2.3904,2.048,0.5981,1.9393,2.723,1.0028,3.1273,2.518,2.6876,2.4628,3.1907,2.8355,3.4546,3.1019,1.2987,3.1214,2.5219,2.8061,1.1253,1.4166,1.5347,2.7729,3.0826,3.032,3.4903,4.0568,1.2618,2.5756,3.5834,1.8629,2.2954,1.5,1.8169,2.9084,1.8193,1.4202,3.9538,2.5224,2.5515,3.2789,2.2086,0.9986,1.1681,4.7444,3.3641,1.7474,1.566,1.193,2.3974,3.74,2.443,3.6657,2.5282,2.5874,2.71,3.3943,1.6563,2.5211,2.4379,3.3178,2.7539,0.362,2.8232,2.4022,3.4708,3.6824,4.0852,2.943,2.8325,1.8846,0.9273,3.433,2.2472,2.6404,2.2777,2.2232,6.8672,0.7403,0.6039,0.6873,2.4054,1.7079,0.5987,1.0023,0.6025,0.451,0,3.5175,1.1473,1.4555,0.8107,0.816,1.9266,0.5615,2.1521,2.975,1.3274,3.9258,4.2789,3.3281,1.7928,2.3629,2.5646,1.0556,3.6946,3.1651,2.2374,2.934,2.3679,0,4.0514,0.3701,3.1668,3.1904,3.6775,2.2947,3.1931,0.228,2.9109,3.5889,3.1598,0,3.2288,4.681,2.9687,0.8769,2.9422,3.0285,2.0846,2.7341,3.7508,3.3031,2.0749,3.0226,0,0,1.1273,1.974,2.2154,0.5856,1.4513,1.2415,0.6247,0.875,1.6062,2.293,0.7298,2.43,2.8741,2.5544,3.308,4.3872,3.1856,1.2937,0.6701,1.8299,2.4828,0.7843,2.0031,3.8168,2.2644,4.0896,1.7334,1.3883,4.0799,4.6869,2.464,3.9831,0.9631,1.2663,1.6207,3.0514,2.1407,3.7393,2.8874,2.274,0.8525,0.9588,3.6787,1.276,3.8974,1.3237,2.2624,3.9905,1.1546,2.4732,1.4626,2.3207,1.2602,2.2484,0.5119,2.3022,1.4169,0.6033,1.3811,0.2097,1.1191,1.5984,1.3749,1.249,1.4643,0.2122,1.8203,1.4632,1.9488,0.2753,2.2228,1.107,1.0588,1.7049,1.6605,0.8982,1.1741,0.7422,0.6413,0.6334,0.7885,0,0.3099,1.2526,0.6549,1.8844,1.6298,2.0146,0.9184,3.1037,1.7918,1.443,1.9414,2.8706,0.3938,1.8376,1.2442,0.2995,1.0212,0,1.2278,1.7187,2.343,0.9508,1.0355,1.5482,3.2575,1.4793,1.7871,2.0334,4.4675,0.5715,1.5046,0.9017,0.9791,2.2038,1.6206,0,0.7057,1.657,2.3356,1.9547,1.5793,0.8585,0.4018,1.556,1.8716,0,0.3172,1.053,1.8877,0.7096,1.6771,2.7165,2.1782,3.2721,1.2282,0.8355,1.5366,1.2952,1.3614,1.7392,1.9305,1.1155,0.2794,0.5213,2.6244,2.7269,0.6315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diff --git a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv
deleted file mode 100644
index c6a0e6b..0000000
--- a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MDA__RPPA__MDA_RPPA__01_28_2016__BI__Gene__Firehose_RPPA.cct.csv
+++ /dev/null
@@ -1,176 +0,0 @@
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diff --git a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv b/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv
deleted file mode 100644
index 07396ab..0000000
--- a/bioneuralnet/datasets/cancer_data/Human__TCGA_BRCA__MS__Clinical__Clinical__01_28_2016__BI__Clinical__Firehose.tsi.csv
+++ /dev/null
@@ -1,21 +0,0 @@
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-years_to_birth,42,63,40,39,53,62,71,44,66,79,71,55,72,65,53,63,50,48,74,80,81,81,59,71,61,61,69,73,84,68,65,90,88,74,72,56,78,70,51,51,55,52,50,70,49,62,46,47,78,72,63,62,87,NA,57,43,89,63,52,50,78,72,59,48,53,54,79,61,56,84,49,61,63,82,64,66,NA,61,55,50,62,52,50,52,70,59,56,54,61,39,52,39,54,77,50,67,45,66,36,48,36,62,47,50,34,60,37,40,85,60,54,73,71,73,41,67,52,63,46,60,76,45,42,37,73,70,54,56,64,63,52,58,79,80,53,46,62,62,66,63,82,48,62,59,55,66,37,62,39,50,51,59,63,48,67,63,53,62,39,61,48,67,56,74,62,61,63,49,85,50,70,39,50,90,34,66,55,47,62,45,35,46,42,49,49,46,57,49,67,46,77,66,62,59,57,78,79,57,37,62,56,63,46,62,44,61,50,49,71,62,62,79,70,40,56,62,52,54,54,84,62,47,51,48,68,48,66,60,63,63,69,77,66,84,42,66,69,69,57,65,65,69,35,58,51,68,80,73,66,76,85,80,58,67,44,59,89,52,65,41,66,53,52,89,45,70,52,64,42,76,74,48,75,45,73,65,76,40,58,47,79,75,57,83,82,68,51,70,62,83,84,66,73,64,57,80,54,83,62,50,76,90,84,78,41,65,62,NA,79,45,87,65,58,76,48,46,79,87,68,79,53,50,75,66,74,66,63,76,74,58,79,47,73,49,43,51,56,72,72,75,71,74,66,29,68,62,36,51,58,79,76,41,56,55,70,62,71,32,59,88,62,61,55,63,58,67,52,55,45,56,61,68,59,69,55,63,54,54,67,36,34,29,59,69,54,57,42,31,41,41,67,41,48,61,71,61,61,36,50,64,59,53,68,49,54,59,40,38,72,39,61,29,47,63,42,43,51,36,75,69,46,77,84,51,69,61,45,48,84,49,43,27,68,46,53,35,66,50,64,54,43,73,36,47,59,54,51,47,83,70,60,46,47,80,49,54,62,39,68,65,64,65,65,46,26,38,54,43,47,49,45,61,46,47,52,55,52,57,58,51,50,50,62,45,69,55,40,62,49,65,46,59,68,54,68,73,45,49,49,62,51,64,90,38,50,81,42,63,75,45,66,74,40,61,26,51,44,60,57,60,71,73,68,38,39,40,42,58,61,67,58,40,40,50,54,48,62,61,62,67,68,63,57,62,50,43,47,47,55,44,76,51,45,52,56,62,47,56,66,53,65,40,42,64,44,51,60,47,56,73,41,35,54,54,76,39,59,55,56,78,71,59,62,61,47,48,58,62,30,63,63,49,64,60,42,71,41,54,62,43,38,52,49,69,79,53,69,68,51,67,62,69,46,45,82,65,69,31,55,77,78,81,56,67,48,65,56,52,62,54,60,59,58,46,77,49,82,50,81,53,56,46,50,39,88,60,56,50,79,70,72,48,78,68,35,55,83,45,38,67,79,80,53,51,89,60,78,68,31,88,47,66,69,44,34,73,44,64,60,78,80,48,77,50,46,45,75,80,41,51,63,46,61,58,54,53,80,67,70,58,50,55,78,NA,46,55,49,62,NA,45,52,82,56,65,52,64,34,54,NA,NA,NA,NA,53,NA,74,56,34,47,58,58,90,NA,NA,63,56,61,48,48,60,68,59,53,49,49,52,51,62,40,74,51,80,45,77,48,60,54,59,41,62,79,62,56,54,54,90,72,59,80,73,77,70,51,81,80,40,62,66,64,62,85,36,45,86,76,55,34,57,56,69,56,48,61,56,84,53,74,81,65,80,71,61,66,40,75,72,58,47,49,59,36,64,41,53,62,55,54,56,68,88,79,53,79,34,54,64,64,41,67,54,64,47,37,76,79,50,62,65,52,74,53,78,55,35,64,48,56,51,68,58,61,50,45,61,47,40,64,76,38,44,51,58,65,66,61,64,34,65,63,50,45,74,46,44,53,61,74,45,80,51,57,71,78,60,37,60,44,40,40,59,41,71,59,57,84,74,46,77,43,69,47,48,44,69,90,70,41,45,52,48,58,58,61,75,28,60,62,71,51,61,58,51,70,66,63,63,64,48,40,56,67,74,68,62,63,43,45,42,58,37,74,71,38,56,47,42,52,54,47,59,51,45,69,71,72,63,49,78,33,48,85,63,60,65,41,63,45,80,48,38,53,38,50,61,70,56,58,43,63,56,55,68,43,77,64,59,58,59,70,66,61,56,50,53,52,59,41,39,52,60,61,75,51,34,59,62,60,32,63,58,69,46,62,57,53,58,52,58,50,57,58,63,72,46,49,44,67,63,65,56,45,NA,NA,44,52,54,73,62,64,88,46,50,90,77,50,67,55,49,70,61,69,73,44,59,71,70,40,61,43,40,51,52,57,66,74,36,80,72,71,59,39,75,43,49,48,67,72,64,41,54,35,30,29,64,71,29,63,65,67,47,51,73,63,75,NA,46,68,46
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atinglobularcarcinoma,infiltratinglobularcarcinoma,infiltratinglobularcarcinoma,infiltratinglobularcarcinoma
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From 688d3c194f42b92c4f0e5d72fbd781ba3e4e9105 Mon Sep 17 00:00:00 2001
From: ramosv <ramosvicente255@gmail.com>
Date: Sun, 16 Feb 2025 15:50:57 -0700
Subject: [PATCH 26/26] Prepare for Release v0.2.0b2

---
 .gitignore                                    |   18 +-
 .readthedocs.yaml                             |    2 +-
 CHANGELOG.md                                  |   37 +
 README.md                                     |  336 +--
 TOPMED.ipynb                                  | 2590 +++++++++++++++++
 assets/enchaned_w_omics.png                   |  Bin 0 -> 35769 bytes
 bioneuralnet/__init__.py                      |   66 +-
 bioneuralnet/clustering/correlated_louvain.py |   67 +-
 .../clustering/correlated_pagerank.py         |    1 +
 bioneuralnet/clustering/hybrid_louvain.py     |  120 -
 bioneuralnet/datasets/dataset_loader.py       |  101 +-
 bioneuralnet/datasets/example1/Y.csv          |    2 +-
 .../datasets/example1/clinical_data.csv       |  359 +++
 bioneuralnet/downstream_task/dpmon.py         |  153 +-
 bioneuralnet/external_tools/SmCCNet.R         |  219 +-
 bioneuralnet/external_tools/__init__.py       |    8 -
 .../external_tools/dynamic_visualization.py   |   87 -
 .../external_tools/feature_selector.py        |  155 -
 bioneuralnet/external_tools/hierarchical.py   |  197 --
 bioneuralnet/external_tools/node2vec.py       |    3 +-
 bioneuralnet/external_tools/smccnet.py        |  113 +-
 .../external_tools/static_visualization.py    |  118 -
 bioneuralnet/metrics/__init__.py              |    9 +-
 bioneuralnet/metrics/check_variance.py        |   96 +
 bioneuralnet/metrics/correlation.py           |   88 +-
 bioneuralnet/metrics/f1_score.py              |   56 -
 bioneuralnet/metrics/mse.py                   |   32 -
 bioneuralnet/metrics/precision.py             |   46 -
 bioneuralnet/metrics/recall.py                |   46 -
 .../network_embedding/gnn_embedding.py        |  106 +-
 .../subject_representation.py                 |  121 +-
 bioneuralnet/utils/__init__.py                |    5 +-
 bioneuralnet/utils/convert_Rdata_to_csv.R     |   24 -
 bioneuralnet/utils/notebook_example.py        |  301 ++
 bioneuralnet/utils/path_utils.py              |    1 -
 bioneuralnet/utils/rdata_to_csv.R             |   39 +
 bioneuralnet/utils/rdata_to_csv.py            |   22 +-
 docs/requirements.txt                         |    2 +
 docs/source/TOPMED.ipynb                      | 2590 +++++++++++++++++
 ...euralnet.clustering.correlated_louvain.rst |   12 +
 ...uralnet.clustering.correlated_pagerank.rst |   12 +
 ...bioneuralnet.clustering.hybrid_louvain.rst |   12 +
 .../_autosummary/bioneuralnet.clustering.rst  |   15 +
 .../bioneuralnet.datasets.dataset_loader.rst  |   12 +
 .../_autosummary/bioneuralnet.datasets.rst    |   13 +
 .../bioneuralnet.downstream_task.DPMON.rst    |   13 +-
 .../bioneuralnet.downstream_task.rst          |    2 +-
 ...t.external_tools.dynamic_visualization.rst |   11 -
 ...ralnet.external_tools.feature_selector.rst |   11 -
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 .../bioneuralnet.external_tools.smccnet.rst   |    5 +-
 ...et.external_tools.static_visualization.rst |   11 -
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 .../bioneuralnet.metrics.check_variance.rst   |   19 +
 .../bioneuralnet.metrics.correlation.rst      |   14 +
 .../_autosummary/bioneuralnet.metrics.rst     |   14 +
 ...ralnet.network_embedding.gnn_embedding.rst |    5 +-
 ...neuralnet.network_embedding.gnn_models.rst |    5 +-
 .../bioneuralnet.network_embedding.rst        |    2 +-
 .../bioneuralnet.subject_representation.rst   |    2 +-
 ..._representation.subject_representation.rst |    6 +-
 .../bioneuralnet.utils.logger.rst             |    5 +-
 .../bioneuralnet.utils.notebook_example.rst   |   17 +
 .../bioneuralnet.utils.path_utils.rst         |    5 +-
 .../bioneuralnet.utils.rdata_to_csv.rst       |   12 +
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 docs/source/clustering.rst                    |  154 +-
 docs/source/conf.py                           |    3 +-
 .../examples/correlated_hybrid_louvain.py     |   54 +
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 docs/source/examples/dpmon_example.py         |   96 +-
 .../examples/dynamic_visualization_example.py |   26 -
 docs/source/examples/example_1.py             |  124 +-
 docs/source/examples/example_2.py             |  120 +-
 .../examples/feature_selector_example.py      |   77 -
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 docs/source/examples/hierarchical_example.py  |   45 -
 docs/source/examples/pagerank_example.py      |   59 -
 docs/source/examples/smccnet_example.py       |   83 +-
 .../examples/static_visualization_example.py  |   26 -
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 .../Analysis_and_Visualization.rst            |   32 -
 docs/source/external_tools/index.rst          |    3 -
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 requirements-dev.txt                          |    6 +-
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 tests/test_dataset_dpmon.py                   |   63 -
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 tests/test_feature_selector.py                |  115 -
 tests/test_hierarchical.py                    |   86 -
 tests/test_static_visualization.py            |  143 -
 116 files changed, 8019 insertions(+), 3248 deletions(-)
 create mode 100644 CHANGELOG.md
 create mode 100644 TOPMED.ipynb
 create mode 100644 assets/enchaned_w_omics.png
 create mode 100644 bioneuralnet/datasets/example1/clinical_data.csv
 delete mode 100644 bioneuralnet/external_tools/dynamic_visualization.py
 delete mode 100644 bioneuralnet/external_tools/feature_selector.py
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 create mode 100644 bioneuralnet/metrics/check_variance.py
 delete mode 100644 bioneuralnet/metrics/f1_score.py
 delete mode 100644 bioneuralnet/metrics/mse.py
 delete mode 100644 bioneuralnet/metrics/precision.py
 delete mode 100644 bioneuralnet/metrics/recall.py
 delete mode 100644 bioneuralnet/utils/convert_Rdata_to_csv.R
 create mode 100644 bioneuralnet/utils/notebook_example.py
 create mode 100644 bioneuralnet/utils/rdata_to_csv.R
 create mode 100644 docs/source/TOPMED.ipynb
 create mode 100644 docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.clustering.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.datasets.dataset_loader.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.datasets.rst
 delete mode 100644 docs/source/_autosummary/bioneuralnet.external_tools.dynamic_visualization.rst
 delete mode 100644 docs/source/_autosummary/bioneuralnet.external_tools.feature_selector.rst
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 create mode 100644 docs/source/_autosummary/bioneuralnet.metrics.correlation.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.metrics.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.utils.notebook_example.rst
 create mode 100644 docs/source/_autosummary/bioneuralnet.utils.rdata_to_csv.rst
 create mode 100644 docs/source/_static/ccrcc_search.png
 create mode 100644 docs/source/_static/clustercorrelation.png
 create mode 100644 docs/source/_static/cptac_clinical.png
 create mode 100644 docs/source/_static/cptac_genomics.png
 create mode 100644 docs/source/_static/cptac_proteomics.png
 create mode 100644 docs/source/_static/cptac_search.png
 create mode 100644 docs/source/_static/cptac_village.PNG
 create mode 100644 docs/source/_static/hybrid_clustering.png
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 create mode 100644 docs/source/examples/correlated_hybrid_louvain.py
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diff --git a/.gitignore b/.gitignore
index ff3314a..09e1627 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@ __pycache__/
 *.pyd
 env/
 venv/
+.test_env/
 ENV/
 env.bak/
 venv.bak/
@@ -16,6 +17,18 @@ bioneuralnet.egg-info
 /docker_files/
 .mypy_cache
 
+# Other example data and tests not needed in the repo.
+TOPMed_testing/
+TOPMed_notes/
+
+todo_slides.md
+test_notes.md
+todo.md
+test_release.md
+test_cuda_issues.md
+dpmon/
+dp/
+
 # Blocking sensitive file types globally
 *.csv
 *.RData
@@ -25,11 +38,6 @@ bioneuralnet.egg-info
 !bioneuralnet/datasets/example1/
 !bioneuralnet/datasets/example1/**/*.csv
 
-dpmon_output/
-todo.md
-ignore_dir/
-
-
 # Sphinx documentation build
 docs/build/
 
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 28dae9d..a3fd812 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -1,7 +1,7 @@
 # Read the Docs configuration file
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
 
-# Required
+# The format is based on https://docs.readthedocs.io/en/stable/config-file/v2.html
 version: 2
 
 build:
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..40269a1
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,37 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),  
+and this project adheres to [Semantic Versioning](https://semver.org/).
+
+## [0.2.0b2] - 2025-02-16
+
+### **Added**
+- **Hybrid Louvain Clustering**: New iterative Louvain clustering method with PageRank refinement.
+- **DatasetLoader Integration**: Standardized dataset loading for examples & tutorials.
+- **GNNEmbedding Improvements**: Enhanced tuning capabilities for better model selection.
+- **New Example Workflows**:
+  - **SmCCNet + GNN Embeddings + Subject Representation**
+  - **SmCCNet + DPMON for Disease Prediction**
+  - **Hybrid Louvain Clustering Example**
+- **Updated Tutorials**: Expanded documentation with `nbsphinx` support for `.ipynb` notebooks.
+
+### **Changed**
+- **FAQ Overhaul**: Simplified and updated FAQs based on new features.
+- **Improved Documentation**: Rewrote and updated multiple sections for better clarity.
+- **Updated README.md**:
+  - Improved feature descriptions.
+  - New **quick-start example** for SmCCNet + DPMON.
+  - Cleaner installation instructions.
+- **Refactored Examples**:
+  - Updated **clustering** and **embedding** workflows to match the latest API changes.
+
+### **Fixed**
+- **Bug Fixes**:
+  - Resolved incorrect handling of `tune=True` in Hybrid Louvain.
+  - Addressed inconsistencies in `GraphEmbedding` parameter parsing.
+  - Fixed dataset loading issues in example scripts.
+
+## **[Unreleased]**
+- Multi-Modal Integration
diff --git a/README.md b/README.md
index 92b70ab..ffe26d8 100644
--- a/README.md
+++ b/README.md
@@ -8,311 +8,197 @@
 
 [![Documentation](https://img.shields.io/badge/docs-read%20the%20docs-blue.svg)](https://bioneuralnet.readthedocs.io/en/latest/)
 
-
-## Welcome to [BioNeuralNet Beta 0.1](https://bioneuralnet.readthedocs.io/en/latest/index.html)
+## Welcome to [BioNeuralNet Beta 0.2](https://bioneuralnet.readthedocs.io/en/latest/index.html)
 
 ![BioNeuralNet Logo](/assets/LOGO_WB.png)
 
-**Note:** This is a **beta version** of BioNeuralNet. It is under active development, and certain features
-may be incomplete or subject to change. Feedback and bug reports are highly encouraged to help us
-improve the tool.
+**Note:** This is a **beta version** of BioNeuralNet. We are actively developing new features and improving stability.  
+Feedback and bug reports are highly encouraged!
 
-BioNeuralNet is a Python-based software tool designed to streamline the integration of multi-omics
-data with **Graph Neural Network (GNN)** embeddings. It supports **graph clustering**, **subject representation**,
-and **disease prediction**, enabling advanced analyses of complex multi-omics networks.
+BioNeuralNet is a Python framework for integrating **multi-omics data** with **Graph Neural Networks (GNNs)**.  
+It provides tools for **graph construction, clustering, network embedding, subject representation, and disease prediction**.
 
 ![BioNeuralNet Workflow](assets/BioNeuralNet.png)
 
 ---
 
-## Key Features
+## **Key Features**
 
-BioNeuralNet offers five core steps in a typical workflow:
+BioNeuralNet enables **multi-omics analysis** through **five core steps**:
 
-### 1. Graph Construction
-- **Not** performed internally. You provide or build adjacency matrices externally (e.g., via **WGCNA**, **SmCCNet**, or your own scripts).
-- Lightweight wrappers are available in `bioneuralnet.external_tools` (e.g., WGCNA, SmCCNet) for convenience. However, using these wrappers is **optional** and not mandatory for BioNeuralNet’s pipeline.
+### 1. **Graph Construction**
+- Build multi-omics networks using **SmCCNet** or custom adjacency matrices.
 
-### 2. Graph Clustering
-- Identify functional modules or communities using **PageRank**.
-- The `PageRank` module enables finding subnetwork clusters through personalized sweep cuts, capturing local neighborhoods influenced by seed nodes.
+### 2. **Graph Clustering**
+- Identify meaningful communities with **Correlated Louvain**, **Hybrid Louvain**, or **Correlated PageRank**.
 
-### 3. Network Embedding
-- Generate embeddings using methods like **GCN**, **GAT**, **GraphSAGE**, and **GIN**.
-- You can attach numeric labels to nodes or remain “unsupervised,” relying solely on graph structure and node features (e.g., correlation with clinical data).
+### 3. **GNN Embedding**
+- Generate node embeddings using models like **GCN, GAT, GraphSAGE, and GIN**.
 
-### 4. Subject Representation
-- Integrate node embeddings back into omics data, enriching each subject’s feature vector by weighting columns with the learned embedding scalars.
+### 4. **Subject Representation**
+- Integrate GNN-based embeddings into omics data via **GraphEmbedding**.
 
-### 5. Downstream Tasks
-- Perform advanced analyses, such as disease prediction, via **DPMON**, which trains a GNN end-to-end alongside a classifier to incorporate both local and global network information.
+### 5. **Disease Prediction**
+- Use **DPMON**, a GNN-powered classifier, to predict disease phenotypes.
 
 ---
 
-# Installation
-
-BioNeuralNet supports Python 3.10 and 3.11 in this beta release. Follow the steps below to set up BioNeuralNet and its dependencies.
+## **Installation**
 
-## 1. Install BioNeuralNet via pip
-
-To install the core BioNeuralNet modules for GNN embeddings, subject representation, disease prediction (DPMON), and clustering:
+BioNeuralNet supports **Python 3.10 and 3.11**.  
 
+### **1. Install BioNeuralNet**
 ```bash
-pip install bioneuralnet==0.1.0b1
+pip install bioneuralnet
 ```
 
-## 2. Install PyTorch and PyTorch Geometric (Separately)
-
-BioNeuralNet relies on PyTorch and PyTorch Geometric for GNN operations:
+### **2. Install PyTorch and PyTorch Geometric**
+BioNeuralNet relies on PyTorch for GNN computations. Install PyTorch **separately**:
 
-- Install PyTorch:
+- **PyTorch (CPU)**:
   ```bash
   pip install torch torchvision torchaudio
   ```
-- Install PyTorch Geometric:
+
+- **PyTorch Geometric**:
   ```bash
   pip install torch_geometric
   ```
 
-For GPU-accelerated builds or other configurations, visit their official guides:
+For GPU acceleration, visit:
 - [PyTorch Installation Guide](https://pytorch.org/get-started/locally/)
 - [PyTorch Geometric Installation Guide](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
 
-Select the appropriate build for your system (e.g., Stable, Linux, pip, Python, CPU/GPU).
-
-![PyTorch Installation](assets/pytorch.png)
-*PyTorch Installation Guide*
-
-![PyTorch Geometric Installation](assets/geometric.png)
-*PyTorch Geometric Installation Guide*
-
-## 3. (Optional) Install R and External Tools
-
-If you plan to use **WGCNA** or **SmCCNet** for network construction:
-
-- Install R from [The R Project](https://www.r-project.org/).
-- Install the required R packages. Open R and run the following:
-
-```r
-if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
-install.packages(c("dplyr", "jsonlite"))
-BiocManager::install(c("impute", "preprocessCore", "GO.db", "AnnotationDbi"))
-install.packages("SmCCNet")
-install.packages("WGCNA")
-```
-
-## 4. Additional Notes for External Tools
-
-For **Node2Vec**, **feature selection**, or **visualization modules**, refer to the external tools documentation in `bioneuralnet.external_tools`. Examples include:
-- **`Node2Vec`**: Node2Vec-based embeddings.
-- **`FeatureSelector`**: Basic feature selection strategies like LassoCV and random forest.
-- **`HierarchicalClustering`**: Agglomerative clustering and silhouette scoring.
-- **`StaticVisualizer` and `DynamicVisualizer`**: Static or interactive network visualization.
-- **`SmCCNet` / `WGCNA`**: Build adjacency matrices using R-based libraries.
-
-These integrations are **optional** and do **not** form part of the core pipeline.
-
-## 5. Development Setup (Optional)
-
-If you plan to contribute to BioNeuralNet:
-
-```bash
-git clone https://github.com/UCD-BDLab/BioNeuralNet.git
-cd BioNeuralNet
-pip install -r requirements-dev.txt
-pre-commit install
-pytest
-```
-
 ---
 
-## Quick Example: Transforming Multi-Omics for Enhanced Disease Prediction
-
-![BioNeuralNet Overview](assets/Overview.png)
-*BioNeuralNet: Transforming Multi-Omics for Enhanced Disease Prediction*
-
-### External Tools
-
-We offer a number of external tools available through the `bioneuralnet.external_tools` module:
-- These tools were implemented to facilitate testing and should not be considered part of the package's core functionality.
-- The classes inside the `external_tools` module are lightweight wrappers around existing tools and libraries offering minimal functionality.
-- We highly encourage users to explore these tools outside of BioNeuralNet to fully leverage their capabilities.
-
-### Steps
-
-Below is a quick example demonstrating the following:
-
-1. **Building or Importing a Network Adjacency Matrix**:
-   - For instance, using external tools like **SmCCNet**.
-
-2. **Using DPMON for Disease Prediction**:
-   - A detailed explanation follows.
-
-#### 1. Data Preparation
-- Input your multi-omics data (e.g., proteomics, metabolomics, genomics) along with phenotype data.
-
-#### 2. Network Construction
-- **Not performed internally**: You need to provide or build adjacency matrices externally (e.g., via WGCNA, SmCCNet, or your own scripts).
-- Lightweight wrappers are available in `bioneuralnet.external_tools` (e.g., WGCNA, SmCCNet) for convenience. However, using these wrappers is **optional** and not mandatory for BioNeuralNet’s pipeline.
-
-#### 3. Disease Prediction
-- **DPMON** integrates GNN-based node embeddings with a downstream neural network to predict disease phenotypes.
-
-### Code Example:
+## **Quick Example: SmCCNet + DPMON for Disease Prediction**
 
 ```python
+import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
 from bioneuralnet.external_tools import SmCCNet
 from bioneuralnet.downstream_task import DPMON
-import pandas as pd
 
-# 1) Prepare data
-omics_data = pd.read_csv("data/omics_data.csv")
-phenotype_data = pd.read_csv("data/phenotype_data.csv")
-clinical_data = pd.read_csv("data/clinical_data.csv")
+# 1) Load dataset
+loader = DatasetLoader("example1")
+omics1, omics2, phenotype, clinical = loader.load_data()
 
-# 2) Run SmCCNet to get adjacency
+# 2) Generate adjacency matrix using SmCCNet
 smccnet = SmCCNet(
-   phenotype_df=phenotype_data,
-   omics_df=omics_data,
-   data_types=["genes, proteins"]
-   kfolds=5,
-   summarization = "NetSHy",
-   seed: 127,
-   )
-adjacency_matrix = smccnet.run()
-
-# 3) Disease Prediction with DPMON
+    phenotype_df=phenotype,
+    omics_dfs=[omics1, omics2],
+    data_types=["genes", "proteins"],
+    kfold=3,
+    subSampNum=500,
+)
+global_network, _ = smccnet.run()
+
+# 3) Run Disease Prediction using DPMON
 dpmon = DPMON(
-   adjacency_matrix=adjacency_matrix,
-   omics_list=[omics_data],
-   phenotype_data=phenotype_data,
-   clinical_data=clinical_data,
-   model: "GAT",
-   gnn_hidden_dim: 64,
-   layer_num: 3,
-   nn_hidden_dim1: 2,
-   nn_hidden_dim2: 2,
-   epoch_num: 10,
-   repeat_num: 5,
-   lr: 0.01,
-   weight_decay: 1e-4,
-   tune: True,
-   gpu: False
+    adjacency_matrix=global_network,
+    omics_list=[omics1, omics2],
+    phenotype_data=phenotype,
+    clinical_data=clinical,
+    tune=True,
 )
-predictions = dpmon.run()
-print("Disease predictions:\n", predictions)
+dpmon_predictions = dpmon.run()
+print("Disease Predictions:\n", dpmon_predictions.head())
 ```
 
-### Output
-- **Adjacency Matrix**: The multi-omics network representation.
-- **Predictions**: Disease phenotype predictions for each sample as a DataFrame linking subjects to predicted classes.
+### **Output**
+- **Adjacency Matrix**: The constructed multi-omics network.
+- **Predictions**: Disease phenotype predictions.
 
 ---
 
-## Documentation & Tutorials
-
-- Extensive documentation at [Read the Docs](https://bioneuralnet.readthedocs.io/en/latest/index.html)
-- Tutorials illustrating:
-  - Unsupervised vs. label-based GNN usage
-  - PageRank clustering and hierarchical clustering
-  - Subject representation
-  - Integrating external tools like WGCNA, SmCCNet
-
-## Frequently Asked Questions (FAQ)
-
-Key topics include:
-- **GPU acceleration** vs. CPU-only
-- **External Tools** usage (R-based adjacency construction, Node2Vec, etc.)
-- **DPMON** for local/global structure-based disease prediction
-- **PageRank or HierarchicalClustering** for subnetwork identification
-
-See [FAQ](https://bioneuralnet.readthedocs.io/en/latest/faq.html) for more.
+## **Documentation & Tutorials**
+- Full documentation: [Read the Docs](https://bioneuralnet.readthedocs.io/en/latest/)
+- Tutorials include:
+  - Multi-omics graph construction
+  - GNN embeddings for disease prediction
+  - Subject representation with integrated embeddings
+  - Clustering using Hybrid Louvain & Correlated PageRank
 
 ---
 
-## Acknowledgments
+## **Frequently Asked Questions (FAQ)**
 
-BioNeuralNet relies on and interfaces with various open-source libraries. We extend our gratitude to the developers and contributors of these projects for their invaluable tools and resources.
+- Does BioNeuralNet support **GPU acceleration**?  
+   - Yes, install PyTorch with CUDA support.
 
-### Core Dependencies
+- Can I use my own **adjacency matrix**?  
+ - yes, you can provide a custom matrix instead of using SmCCNet.
 
-- [PyYAML](https://pyyaml.org/) - **MIT License**
-- [pandas](https://pandas.pydata.org/) - **BSD 3-Clause License**
-- [numpy](https://numpy.org/) - **BSD 3-Clause License**
-- [scikit-learn](https://scikit-learn.org/) - **BSD 3-Clause License**
-- [node2vec](https://github.com/aditya-grover/node2vec) - **MIT License**
-- [matplotlib](https://matplotlib.org/) - **Matplotlib License**
-- [ray](https://github.com/ray-project/ray) - **Apache 2.0 License**
-- [tensorboardX](https://github.com/lanpa/tensorboardX) - **MIT License**
-- [networkx](https://networkx.org/) - **BSD License**
-- [pyvis](https://github.com/WestHealth/pyvis) - **MIT License**
-- [leidenalg](https://github.com/vtraag/leidenalg) - **GNU LGPL v3**
-- [dtt](https://github.com/BioroboticsLab/dtt) - **MIT License**
-- [pyreadr](https://github.com/ofajardo/pyreadr) - **MIT License**
-- [torch](https://pytorch.org/) - **BSD License**
-- [torch_geometric](https://github.com/pyg-team/pytorch_geometric) - **MIT License**
+- What clustering methods are supported?  
+   - **Correlated Louvain**, **Hybrid Louvain**, and **Correlated PageRank**.
 
-### Development Dependencies
+See the full [FAQ](https://bioneuralnet.readthedocs.io/en/latest/faq.html).
 
-These tools are essential for the development and maintenance of BioNeuralNet but are not required for end-users.
-
-- [pytest](https://pytest.org/) - **MIT License**
-- [pytest-cov](https://pytest-cov.readthedocs.io/) - **MIT License**
-- [pytest-mock](https://github.com/pytest-dev/pytest-mock) - **MIT License**
-- [Sphinx](https://www.sphinx-doc.org/) - **BSD License**
-- [Sphinx RTD Theme](https://sphinx-rtd-theme.readthedocs.io/) - **BSD License**
-- [sphinx-autosummary-accessors](https://github.com/kennethreitz/sphinx-autosummary-accessors) - **MIT License**
-- [sphinxcontrib-napoleon](https://sphinxcontrib-napoleon.readthedocs.io/) - **BSD License**
-- [flake8](https://flake8.pycqa.org/) - **MIT License**
-- [Black](https://black.readthedocs.io/) - **MIT License**
-- [mypy](http://mypy-lang.org/) - **MIT License**
-- [pre-commit](https://pre-commit.com/) - **MIT License**
-- [tox](https://tox.readthedocs.io/) - **MIT License**
-- [setuptools](https://setuptools.pypa.io/) - **MIT License**
-- [twine](https://twine.readthedocs.io/) - **MIT License**
+---
 
-### External Tools
+## **Acknowledgments**
 
-BioNeuralNet integrates with external tools to enhance functionality:
+BioNeuralNet integrates multiple open-source libraries. We acknowledge key dependencies:
 
-- [WGCNA](https://cran.r-project.org/package=WGCNA) - **GPL-3.0 License**
-- [SmCCNet](https://cran.r-project.org/package=SmCCNet) - **GPL-3.0 License**
+- **PyTorch** - GNN computations and deep learning models.  
+- **PyTorch Geometric** - Graph-based learning for multi-omics.  
+- **NetworkX** - Graph data structure and algorithms.  
+- **Scikit-learn** - Feature selection and evaluation utilities.  
+- **pandas & numpy** - Core data processing tools.  
+- **ray[tune]** - Hyperparameter tuning for GNN models.  
+- **matplotlib** - Data visualization.  
+- **cptac** - Dataset handling for clinical proteomics.  
+- **python-louvain** - Community detection algorithms.  
 
-### Special Thanks
+We also acknowledge R-based tools for external network construction:
+- **SmCCNet** - Sparse multiple canonical correlation network.
+- **WGCNA** - Weighted gene co-expression network analysis.
 
-We appreciate the efforts of these communities and all contributors who make open-source development possible. Your dedication and hard work enable projects like BioNeuralNet to thrive and evolve.
+These tools **enhance BioNeuralNet** but are **not required** for core functionality.
 
 ---
 
-## Testing & CI
+## **Testing & Continuous Integration**
 
-1. **Local Testing**:
+1. **Run Tests Locally**:
    ```bash
    pytest --cov=bioneuralnet --cov-report=html
    open htmlcov/index.html
    ```
 
 2. **Continuous Integration**:
-   - GitHub Actions run our test suite and code checks on each commit and PR.
+   - GitHub Actions runs automated tests on each commit.
 
-## Contributing
+---
 
-- **Fork** the repository, create a new branch, implement your changes.
-- **Add/Update** tests, docstrings, and examples if appropriate.
-- **Open** a pull request describing your modifications.
+## **Contributing**
 
-For more details, see our [FAQ](https://bioneuralnet.readthedocs.io/en/latest/faq.html)
-or open an [issue](https://github.com/UCD-BDLab/BioNeuralNet/issues).
+We welcome contributions!  
+To get started:
 
-## License
+```bash
+git clone https://github.com/UCD-BDLab/BioNeuralNet.git
+cd BioNeuralNet
+pip install -r requirements-dev.txt
+pre-commit install
+pytest
+```
 
-- **License:** [MIT License](https://github.com/UCD-BDLab/BioNeuralNet/blob/main/LICENSE)
+### **How to Contribute**
+- **Fork** the repo, create a new branch, implement your changes.
+- **Add tests and documentation** for your new feature.
+- **Submit a pull request** with a clear description.
 
-## Contact
+For more details, check our [Contributing Guide](https://github.com/UCD-BDLab/BioNeuralNet/blob/main/CONTRIBUTING.md).
 
-- **Questions or Feature Requests:** [Open an issue](https://github.com/UCD-BDLab/BioNeuralNet/issues)
-- **Email:** [vicente.ramos@ucdenver.edu](mailto:vicente.ramos@ucdenver.edu)
+---
+
+## **License**
+- **License:** [MIT License](https://github.com/UCD-BDLab/BioNeuralNet/blob/main/LICENSE)
 
 ---
 
-BioNeuralNet aims to **streamline** multi-omics network analysis by providing **graph clustering**, **GNN embedding**, **subject representation**, and **disease prediction** tools. We hope it helps uncover new insights in multi-omics research.
+## **Contact**
+- **Issues & Feature Requests:** [Open an Issue](https://github.com/UCD-BDLab/BioNeuralNet/issues)
+- **Email:** [vicente.ramos@ucdenver.edu](mailto:vicente.ramos@ucdenver.edu)
+
diff --git a/TOPMED.ipynb b/TOPMED.ipynb
new file mode 100644
index 0000000..8f1ecb0
--- /dev/null
+++ b/TOPMED.ipynb
@@ -0,0 +1,2590 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TOPMED Presentation\n",
+    "\n",
+    "#### Demonstrates a **step-by-step** guide to using BioNeuralNet for multi-omics analysis and research."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# In terminal use:\n",
+    "pip install bioneuralnet\n",
+    "\n",
+    "# In Jupiter notebook use\n",
+    "!{sys.executable} -m pip install bioneuralnet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.datasets import DatasetLoader\n",
+    "from bioneuralnet.clustering import CorrelatedLouvain\n",
+    "from bioneuralnet.clustering import HybridLouvain\n",
+    "from bioneuralnet.network_embedding import GNNEmbedding\n",
+    "from bioneuralnet.subject_representation import GraphEmbedding\n",
+    "from bioneuralnet.downstream_task import DPMON\n",
+    "from bioneuralnet.metrics import compute_correlation, compute_cluster_correlation_from_df, convert_louvain_to_adjacency\n",
+    "from bioneuralnet.metrics import CheckVariance, network_remove_low_variance, network_remove_high_zero_fraction\n",
+    "\n",
+    "from bioneuralnet.external_tools import SmCCNet\n",
+    "from bioneuralnet.external_tools import WGCNA\n",
+    "from bioneuralnet.utils.notebook_example import plot_embeddings, plot_network, plot_performance, compare_clusters\n",
+    "from bioneuralnet.utils import get_logger"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- #### Your dataset import may look something like this.\n",
+    "- #### After loading your data, the remaining steps will be the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "genomic = pd.read_csv( \"genes.csv\")\n",
+    "proteomics = pd.read_csv(\"proteins.csv\")\n",
+    "metabolites = pd.read_csv(\"metabolites.csv\")\n",
+    "phenotype = pd.read_csv(\"phenotype.csv\")\n",
+    "clinical_data = pd.read_csv(\"clinical.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene_1</th>\n",
+       "      <th>Gene_2</th>\n",
+       "      <th>Gene_3</th>\n",
+       "      <th>Gene_4</th>\n",
+       "      <th>Gene_5</th>\n",
+       "      <th>Gene_6</th>\n",
+       "      <th>Gene_7</th>\n",
+       "      <th>Gene_8</th>\n",
+       "      <th>Gene_9</th>\n",
+       "      <th>Gene_10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Gene_491</th>\n",
+       "      <th>Gene_492</th>\n",
+       "      <th>Gene_493</th>\n",
+       "      <th>Gene_494</th>\n",
+       "      <th>Gene_495</th>\n",
+       "      <th>Gene_496</th>\n",
+       "      <th>Gene_497</th>\n",
+       "      <th>Gene_498</th>\n",
+       "      <th>Gene_499</th>\n",
+       "      <th>Gene_500</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>22.485701</td>\n",
+       "      <td>40.353720</td>\n",
+       "      <td>31.025745</td>\n",
+       "      <td>20.847206</td>\n",
+       "      <td>26.697293</td>\n",
+       "      <td>30.205449</td>\n",
+       "      <td>23.512005</td>\n",
+       "      <td>33.677622</td>\n",
+       "      <td>19.430333</td>\n",
+       "      <td>30.260153</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.967210</td>\n",
+       "      <td>13.334132</td>\n",
+       "      <td>13.262070</td>\n",
+       "      <td>13.608147</td>\n",
+       "      <td>13.338606</td>\n",
+       "      <td>15.247613</td>\n",
+       "      <td>13.603157</td>\n",
+       "      <td>14.727795</td>\n",
+       "      <td>13.400950</td>\n",
+       "      <td>12.769172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>37.058850</td>\n",
+       "      <td>34.052233</td>\n",
+       "      <td>33.487020</td>\n",
+       "      <td>23.531461</td>\n",
+       "      <td>26.754628</td>\n",
+       "      <td>31.735945</td>\n",
+       "      <td>22.795952</td>\n",
+       "      <td>29.301536</td>\n",
+       "      <td>14.936397</td>\n",
+       "      <td>30.823015</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.886247</td>\n",
+       "      <td>13.241386</td>\n",
+       "      <td>12.445773</td>\n",
+       "      <td>13.400288</td>\n",
+       "      <td>12.587993</td>\n",
+       "      <td>15.048676</td>\n",
+       "      <td>12.018609</td>\n",
+       "      <td>14.513958</td>\n",
+       "      <td>12.066379</td>\n",
+       "      <td>12.583460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>20.530767</td>\n",
+       "      <td>31.669623</td>\n",
+       "      <td>35.189567</td>\n",
+       "      <td>20.952544</td>\n",
+       "      <td>25.018826</td>\n",
+       "      <td>32.157235</td>\n",
+       "      <td>25.069464</td>\n",
+       "      <td>22.853719</td>\n",
+       "      <td>18.220225</td>\n",
+       "      <td>23.092805</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.198357</td>\n",
+       "      <td>12.869640</td>\n",
+       "      <td>12.841290</td>\n",
+       "      <td>13.127437</td>\n",
+       "      <td>12.607377</td>\n",
+       "      <td>14.398177</td>\n",
+       "      <td>12.554904</td>\n",
+       "      <td>14.339382</td>\n",
+       "      <td>12.891962</td>\n",
+       "      <td>12.760553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>33.186888</td>\n",
+       "      <td>38.480880</td>\n",
+       "      <td>18.897097</td>\n",
+       "      <td>31.823300</td>\n",
+       "      <td>34.049383</td>\n",
+       "      <td>38.799887</td>\n",
+       "      <td>24.106468</td>\n",
+       "      <td>12.397175</td>\n",
+       "      <td>13.724255</td>\n",
+       "      <td>27.703085</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.197502</td>\n",
+       "      <td>13.312536</td>\n",
+       "      <td>11.645099</td>\n",
+       "      <td>13.933684</td>\n",
+       "      <td>12.103087</td>\n",
+       "      <td>15.133432</td>\n",
+       "      <td>12.436027</td>\n",
+       "      <td>15.116888</td>\n",
+       "      <td>12.810732</td>\n",
+       "      <td>12.972879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>28.961981</td>\n",
+       "      <td>41.060494</td>\n",
+       "      <td>28.494956</td>\n",
+       "      <td>18.374495</td>\n",
+       "      <td>30.815238</td>\n",
+       "      <td>24.004535</td>\n",
+       "      <td>29.616296</td>\n",
+       "      <td>24.364045</td>\n",
+       "      <td>11.409338</td>\n",
+       "      <td>33.599828</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.157141</td>\n",
+       "      <td>13.386978</td>\n",
+       "      <td>13.007109</td>\n",
+       "      <td>13.826532</td>\n",
+       "      <td>12.343930</td>\n",
+       "      <td>15.280175</td>\n",
+       "      <td>13.363962</td>\n",
+       "      <td>14.008675</td>\n",
+       "      <td>12.479124</td>\n",
+       "      <td>12.156407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>24.520652</td>\n",
+       "      <td>28.595409</td>\n",
+       "      <td>31.299666</td>\n",
+       "      <td>32.095379</td>\n",
+       "      <td>33.659730</td>\n",
+       "      <td>29.353948</td>\n",
+       "      <td>29.871659</td>\n",
+       "      <td>25.089732</td>\n",
+       "      <td>14.437588</td>\n",
+       "      <td>27.261916</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.748228</td>\n",
+       "      <td>13.096453</td>\n",
+       "      <td>12.768936</td>\n",
+       "      <td>13.228560</td>\n",
+       "      <td>11.595884</td>\n",
+       "      <td>15.251601</td>\n",
+       "      <td>12.055375</td>\n",
+       "      <td>15.530606</td>\n",
+       "      <td>13.644383</td>\n",
+       "      <td>13.018032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>31.252789</td>\n",
+       "      <td>28.988087</td>\n",
+       "      <td>29.574195</td>\n",
+       "      <td>31.189288</td>\n",
+       "      <td>32.098841</td>\n",
+       "      <td>26.849138</td>\n",
+       "      <td>22.619669</td>\n",
+       "      <td>28.006670</td>\n",
+       "      <td>8.615549</td>\n",
+       "      <td>30.838843</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.460876</td>\n",
+       "      <td>12.994504</td>\n",
+       "      <td>13.325608</td>\n",
+       "      <td>13.386970</td>\n",
+       "      <td>12.335143</td>\n",
+       "      <td>14.966097</td>\n",
+       "      <td>13.998975</td>\n",
+       "      <td>13.250821</td>\n",
+       "      <td>12.947672</td>\n",
+       "      <td>13.161434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>24.894826</td>\n",
+       "      <td>25.944887</td>\n",
+       "      <td>30.852641</td>\n",
+       "      <td>26.705158</td>\n",
+       "      <td>30.102546</td>\n",
+       "      <td>37.197952</td>\n",
+       "      <td>35.032309</td>\n",
+       "      <td>34.775576</td>\n",
+       "      <td>13.354611</td>\n",
+       "      <td>29.697348</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.884821</td>\n",
+       "      <td>12.785372</td>\n",
+       "      <td>12.954534</td>\n",
+       "      <td>13.906209</td>\n",
+       "      <td>12.074805</td>\n",
+       "      <td>15.519179</td>\n",
+       "      <td>12.742078</td>\n",
+       "      <td>14.445011</td>\n",
+       "      <td>12.129990</td>\n",
+       "      <td>13.844271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>17.034337</td>\n",
+       "      <td>38.574705</td>\n",
+       "      <td>25.095201</td>\n",
+       "      <td>37.062442</td>\n",
+       "      <td>35.417758</td>\n",
+       "      <td>29.753145</td>\n",
+       "      <td>25.855997</td>\n",
+       "      <td>25.603043</td>\n",
+       "      <td>13.180794</td>\n",
+       "      <td>31.188598</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.278454</td>\n",
+       "      <td>13.445404</td>\n",
+       "      <td>12.457436</td>\n",
+       "      <td>14.036252</td>\n",
+       "      <td>13.412114</td>\n",
+       "      <td>14.158416</td>\n",
+       "      <td>13.284701</td>\n",
+       "      <td>13.662274</td>\n",
+       "      <td>12.943670</td>\n",
+       "      <td>13.996352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>20.839167</td>\n",
+       "      <td>27.099788</td>\n",
+       "      <td>31.038453</td>\n",
+       "      <td>19.410859</td>\n",
+       "      <td>31.818995</td>\n",
+       "      <td>33.493625</td>\n",
+       "      <td>22.769369</td>\n",
+       "      <td>22.869481</td>\n",
+       "      <td>16.839248</td>\n",
+       "      <td>24.644476</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.519057</td>\n",
+       "      <td>13.308512</td>\n",
+       "      <td>12.261076</td>\n",
+       "      <td>11.211687</td>\n",
+       "      <td>12.058898</td>\n",
+       "      <td>15.349873</td>\n",
+       "      <td>13.166209</td>\n",
+       "      <td>15.295902</td>\n",
+       "      <td>13.257230</td>\n",
+       "      <td>13.178058</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 500 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Gene_1     Gene_2     Gene_3     Gene_4     Gene_5     Gene_6  \\\n",
+       "Samp_1    22.485701  40.353720  31.025745  20.847206  26.697293  30.205449   \n",
+       "Samp_2    37.058850  34.052233  33.487020  23.531461  26.754628  31.735945   \n",
+       "Samp_3    20.530767  31.669623  35.189567  20.952544  25.018826  32.157235   \n",
+       "Samp_4    33.186888  38.480880  18.897097  31.823300  34.049383  38.799887   \n",
+       "Samp_5    28.961981  41.060494  28.494956  18.374495  30.815238  24.004535   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  24.520652  28.595409  31.299666  32.095379  33.659730  29.353948   \n",
+       "Samp_355  31.252789  28.988087  29.574195  31.189288  32.098841  26.849138   \n",
+       "Samp_356  24.894826  25.944887  30.852641  26.705158  30.102546  37.197952   \n",
+       "Samp_357  17.034337  38.574705  25.095201  37.062442  35.417758  29.753145   \n",
+       "Samp_358  20.839167  27.099788  31.038453  19.410859  31.818995  33.493625   \n",
+       "\n",
+       "             Gene_7     Gene_8     Gene_9    Gene_10  ...   Gene_491  \\\n",
+       "Samp_1    23.512005  33.677622  19.430333  30.260153  ...  12.967210   \n",
+       "Samp_2    22.795952  29.301536  14.936397  30.823015  ...  11.886247   \n",
+       "Samp_3    25.069464  22.853719  18.220225  23.092805  ...  12.198357   \n",
+       "Samp_4    24.106468  12.397175  13.724255  27.703085  ...  12.197502   \n",
+       "Samp_5    29.616296  24.364045  11.409338  33.599828  ...  14.157141   \n",
+       "...             ...        ...        ...        ...  ...        ...   \n",
+       "Samp_354  29.871659  25.089732  14.437588  27.261916  ...  11.748228   \n",
+       "Samp_355  22.619669  28.006670   8.615549  30.838843  ...  11.460876   \n",
+       "Samp_356  35.032309  34.775576  13.354611  29.697348  ...  12.884821   \n",
+       "Samp_357  25.855997  25.603043  13.180794  31.188598  ...  13.278454   \n",
+       "Samp_358  22.769369  22.869481  16.839248  24.644476  ...  12.519057   \n",
+       "\n",
+       "           Gene_492   Gene_493   Gene_494   Gene_495   Gene_496   Gene_497  \\\n",
+       "Samp_1    13.334132  13.262070  13.608147  13.338606  15.247613  13.603157   \n",
+       "Samp_2    13.241386  12.445773  13.400288  12.587993  15.048676  12.018609   \n",
+       "Samp_3    12.869640  12.841290  13.127437  12.607377  14.398177  12.554904   \n",
+       "Samp_4    13.312536  11.645099  13.933684  12.103087  15.133432  12.436027   \n",
+       "Samp_5    13.386978  13.007109  13.826532  12.343930  15.280175  13.363962   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  13.096453  12.768936  13.228560  11.595884  15.251601  12.055375   \n",
+       "Samp_355  12.994504  13.325608  13.386970  12.335143  14.966097  13.998975   \n",
+       "Samp_356  12.785372  12.954534  13.906209  12.074805  15.519179  12.742078   \n",
+       "Samp_357  13.445404  12.457436  14.036252  13.412114  14.158416  13.284701   \n",
+       "Samp_358  13.308512  12.261076  11.211687  12.058898  15.349873  13.166209   \n",
+       "\n",
+       "           Gene_498   Gene_499   Gene_500  \n",
+       "Samp_1    14.727795  13.400950  12.769172  \n",
+       "Samp_2    14.513958  12.066379  12.583460  \n",
+       "Samp_3    14.339382  12.891962  12.760553  \n",
+       "Samp_4    15.116888  12.810732  12.972879  \n",
+       "Samp_5    14.008675  12.479124  12.156407  \n",
+       "...             ...        ...        ...  \n",
+       "Samp_354  15.530606  13.644383  13.018032  \n",
+       "Samp_355  13.250821  12.947672  13.161434  \n",
+       "Samp_356  14.445011  12.129990  13.844271  \n",
+       "Samp_357  13.662274  12.943670  13.996352  \n",
+       "Samp_358  15.295902  13.257230  13.178058  \n",
+       "\n",
+       "[358 rows x 500 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mir_1</th>\n",
+       "      <th>Mir_2</th>\n",
+       "      <th>Mir_3</th>\n",
+       "      <th>Mir_4</th>\n",
+       "      <th>Mir_5</th>\n",
+       "      <th>Mir_6</th>\n",
+       "      <th>Mir_7</th>\n",
+       "      <th>Mir_8</th>\n",
+       "      <th>Mir_9</th>\n",
+       "      <th>Mir_10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Mir_91</th>\n",
+       "      <th>Mir_92</th>\n",
+       "      <th>Mir_93</th>\n",
+       "      <th>Mir_94</th>\n",
+       "      <th>Mir_95</th>\n",
+       "      <th>Mir_96</th>\n",
+       "      <th>Mir_97</th>\n",
+       "      <th>Mir_98</th>\n",
+       "      <th>Mir_99</th>\n",
+       "      <th>Mir_100</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>15.223913</td>\n",
+       "      <td>17.545826</td>\n",
+       "      <td>15.784719</td>\n",
+       "      <td>14.891983</td>\n",
+       "      <td>10.348205</td>\n",
+       "      <td>9.689755</td>\n",
+       "      <td>11.093630</td>\n",
+       "      <td>13.189246</td>\n",
+       "      <td>12.526561</td>\n",
+       "      <td>10.311609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.551462</td>\n",
+       "      <td>5.407878</td>\n",
+       "      <td>13.933045</td>\n",
+       "      <td>10.287521</td>\n",
+       "      <td>10.213316</td>\n",
+       "      <td>10.609973</td>\n",
+       "      <td>14.554996</td>\n",
+       "      <td>10.955650</td>\n",
+       "      <td>11.422531</td>\n",
+       "      <td>10.862970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>16.306965</td>\n",
+       "      <td>16.672830</td>\n",
+       "      <td>13.361529</td>\n",
+       "      <td>14.488549</td>\n",
+       "      <td>12.660905</td>\n",
+       "      <td>11.333613</td>\n",
+       "      <td>11.254930</td>\n",
+       "      <td>13.457435</td>\n",
+       "      <td>13.279747</td>\n",
+       "      <td>10.428848</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.862413</td>\n",
+       "      <td>7.309226</td>\n",
+       "      <td>13.586180</td>\n",
+       "      <td>11.975544</td>\n",
+       "      <td>11.496937</td>\n",
+       "      <td>10.653742</td>\n",
+       "      <td>14.414225</td>\n",
+       "      <td>11.811004</td>\n",
+       "      <td>12.413667</td>\n",
+       "      <td>10.719110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>16.545119</td>\n",
+       "      <td>16.735005</td>\n",
+       "      <td>14.617472</td>\n",
+       "      <td>17.845267</td>\n",
+       "      <td>13.822790</td>\n",
+       "      <td>11.329333</td>\n",
+       "      <td>11.171749</td>\n",
+       "      <td>12.715107</td>\n",
+       "      <td>13.787771</td>\n",
+       "      <td>10.364577</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.874958</td>\n",
+       "      <td>7.754733</td>\n",
+       "      <td>13.847029</td>\n",
+       "      <td>12.424403</td>\n",
+       "      <td>10.930177</td>\n",
+       "      <td>10.255484</td>\n",
+       "      <td>13.570352</td>\n",
+       "      <td>11.311925</td>\n",
+       "      <td>11.072915</td>\n",
+       "      <td>11.418794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>13.986899</td>\n",
+       "      <td>16.207432</td>\n",
+       "      <td>16.293078</td>\n",
+       "      <td>17.725286</td>\n",
+       "      <td>12.300565</td>\n",
+       "      <td>9.844108</td>\n",
+       "      <td>11.586551</td>\n",
+       "      <td>11.878702</td>\n",
+       "      <td>14.594392</td>\n",
+       "      <td>10.936651</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.615623</td>\n",
+       "      <td>6.693593</td>\n",
+       "      <td>13.840728</td>\n",
+       "      <td>12.245466</td>\n",
+       "      <td>10.836894</td>\n",
+       "      <td>11.502232</td>\n",
+       "      <td>15.483399</td>\n",
+       "      <td>10.812811</td>\n",
+       "      <td>10.121957</td>\n",
+       "      <td>11.039089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>16.338332</td>\n",
+       "      <td>17.393869</td>\n",
+       "      <td>16.397925</td>\n",
+       "      <td>15.853725</td>\n",
+       "      <td>13.387675</td>\n",
+       "      <td>10.599414</td>\n",
+       "      <td>12.355466</td>\n",
+       "      <td>12.450426</td>\n",
+       "      <td>13.778779</td>\n",
+       "      <td>10.405327</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.146865</td>\n",
+       "      <td>8.104206</td>\n",
+       "      <td>13.903452</td>\n",
+       "      <td>11.423350</td>\n",
+       "      <td>9.997107</td>\n",
+       "      <td>10.744586</td>\n",
+       "      <td>14.465583</td>\n",
+       "      <td>11.730166</td>\n",
+       "      <td>12.206151</td>\n",
+       "      <td>10.724849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>15.065065</td>\n",
+       "      <td>16.079830</td>\n",
+       "      <td>14.635616</td>\n",
+       "      <td>17.013845</td>\n",
+       "      <td>11.612843</td>\n",
+       "      <td>9.850100</td>\n",
+       "      <td>11.375670</td>\n",
+       "      <td>12.870558</td>\n",
+       "      <td>13.873811</td>\n",
+       "      <td>11.369083</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.110691</td>\n",
+       "      <td>8.124900</td>\n",
+       "      <td>14.316491</td>\n",
+       "      <td>12.599296</td>\n",
+       "      <td>10.302683</td>\n",
+       "      <td>11.286541</td>\n",
+       "      <td>14.478041</td>\n",
+       "      <td>11.141163</td>\n",
+       "      <td>11.102427</td>\n",
+       "      <td>11.993050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>15.997576</td>\n",
+       "      <td>15.448951</td>\n",
+       "      <td>15.355566</td>\n",
+       "      <td>16.501752</td>\n",
+       "      <td>11.701778</td>\n",
+       "      <td>11.323521</td>\n",
+       "      <td>11.610163</td>\n",
+       "      <td>12.733208</td>\n",
+       "      <td>13.650662</td>\n",
+       "      <td>10.770903</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.711232</td>\n",
+       "      <td>6.977555</td>\n",
+       "      <td>14.805837</td>\n",
+       "      <td>12.283549</td>\n",
+       "      <td>10.847157</td>\n",
+       "      <td>9.833187</td>\n",
+       "      <td>14.037476</td>\n",
+       "      <td>11.082880</td>\n",
+       "      <td>11.708466</td>\n",
+       "      <td>10.654141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>15.206862</td>\n",
+       "      <td>14.395378</td>\n",
+       "      <td>16.218001</td>\n",
+       "      <td>16.044955</td>\n",
+       "      <td>13.650741</td>\n",
+       "      <td>11.057462</td>\n",
+       "      <td>13.324846</td>\n",
+       "      <td>13.070428</td>\n",
+       "      <td>16.109746</td>\n",
+       "      <td>11.253359</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.497926</td>\n",
+       "      <td>6.840175</td>\n",
+       "      <td>15.048896</td>\n",
+       "      <td>12.615673</td>\n",
+       "      <td>10.583973</td>\n",
+       "      <td>12.708186</td>\n",
+       "      <td>16.325254</td>\n",
+       "      <td>10.789635</td>\n",
+       "      <td>10.830833</td>\n",
+       "      <td>10.983455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>14.474129</td>\n",
+       "      <td>15.482863</td>\n",
+       "      <td>15.512549</td>\n",
+       "      <td>15.136613</td>\n",
+       "      <td>14.531277</td>\n",
+       "      <td>10.713539</td>\n",
+       "      <td>11.632242</td>\n",
+       "      <td>13.792981</td>\n",
+       "      <td>13.486404</td>\n",
+       "      <td>10.870028</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.902283</td>\n",
+       "      <td>7.339226</td>\n",
+       "      <td>14.295151</td>\n",
+       "      <td>11.853855</td>\n",
+       "      <td>10.439473</td>\n",
+       "      <td>11.897782</td>\n",
+       "      <td>16.684790</td>\n",
+       "      <td>10.847185</td>\n",
+       "      <td>11.491449</td>\n",
+       "      <td>11.684467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>15.094188</td>\n",
+       "      <td>16.047304</td>\n",
+       "      <td>15.298871</td>\n",
+       "      <td>17.022665</td>\n",
+       "      <td>15.046043</td>\n",
+       "      <td>10.931386</td>\n",
+       "      <td>12.289466</td>\n",
+       "      <td>14.327210</td>\n",
+       "      <td>13.533348</td>\n",
+       "      <td>9.792684</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.017328</td>\n",
+       "      <td>7.814296</td>\n",
+       "      <td>14.267007</td>\n",
+       "      <td>12.117150</td>\n",
+       "      <td>11.832061</td>\n",
+       "      <td>11.021241</td>\n",
+       "      <td>14.533734</td>\n",
+       "      <td>12.015799</td>\n",
+       "      <td>11.551237</td>\n",
+       "      <td>11.221372</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 100 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Mir_1      Mir_2      Mir_3      Mir_4      Mir_5      Mir_6  \\\n",
+       "Samp_1    15.223913  17.545826  15.784719  14.891983  10.348205   9.689755   \n",
+       "Samp_2    16.306965  16.672830  13.361529  14.488549  12.660905  11.333613   \n",
+       "Samp_3    16.545119  16.735005  14.617472  17.845267  13.822790  11.329333   \n",
+       "Samp_4    13.986899  16.207432  16.293078  17.725286  12.300565   9.844108   \n",
+       "Samp_5    16.338332  17.393869  16.397925  15.853725  13.387675  10.599414   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  15.065065  16.079830  14.635616  17.013845  11.612843   9.850100   \n",
+       "Samp_355  15.997576  15.448951  15.355566  16.501752  11.701778  11.323521   \n",
+       "Samp_356  15.206862  14.395378  16.218001  16.044955  13.650741  11.057462   \n",
+       "Samp_357  14.474129  15.482863  15.512549  15.136613  14.531277  10.713539   \n",
+       "Samp_358  15.094188  16.047304  15.298871  17.022665  15.046043  10.931386   \n",
+       "\n",
+       "              Mir_7      Mir_8      Mir_9     Mir_10  ...    Mir_91    Mir_92  \\\n",
+       "Samp_1    11.093630  13.189246  12.526561  10.311609  ...  7.551462  5.407878   \n",
+       "Samp_2    11.254930  13.457435  13.279747  10.428848  ...  6.862413  7.309226   \n",
+       "Samp_3    11.171749  12.715107  13.787771  10.364577  ...  6.874958  7.754733   \n",
+       "Samp_4    11.586551  11.878702  14.594392  10.936651  ...  9.615623  6.693593   \n",
+       "Samp_5    12.355466  12.450426  13.778779  10.405327  ...  9.146865  8.104206   \n",
+       "...             ...        ...        ...        ...  ...       ...       ...   \n",
+       "Samp_354  11.375670  12.870558  13.873811  11.369083  ...  8.110691  8.124900   \n",
+       "Samp_355  11.610163  12.733208  13.650662  10.770903  ...  8.711232  6.977555   \n",
+       "Samp_356  13.324846  13.070428  16.109746  11.253359  ...  7.497926  6.840175   \n",
+       "Samp_357  11.632242  13.792981  13.486404  10.870028  ...  7.902283  7.339226   \n",
+       "Samp_358  12.289466  14.327210  13.533348   9.792684  ...  8.017328  7.814296   \n",
+       "\n",
+       "             Mir_93     Mir_94     Mir_95     Mir_96     Mir_97     Mir_98  \\\n",
+       "Samp_1    13.933045  10.287521  10.213316  10.609973  14.554996  10.955650   \n",
+       "Samp_2    13.586180  11.975544  11.496937  10.653742  14.414225  11.811004   \n",
+       "Samp_3    13.847029  12.424403  10.930177  10.255484  13.570352  11.311925   \n",
+       "Samp_4    13.840728  12.245466  10.836894  11.502232  15.483399  10.812811   \n",
+       "Samp_5    13.903452  11.423350   9.997107  10.744586  14.465583  11.730166   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  14.316491  12.599296  10.302683  11.286541  14.478041  11.141163   \n",
+       "Samp_355  14.805837  12.283549  10.847157   9.833187  14.037476  11.082880   \n",
+       "Samp_356  15.048896  12.615673  10.583973  12.708186  16.325254  10.789635   \n",
+       "Samp_357  14.295151  11.853855  10.439473  11.897782  16.684790  10.847185   \n",
+       "Samp_358  14.267007  12.117150  11.832061  11.021241  14.533734  12.015799   \n",
+       "\n",
+       "             Mir_99    Mir_100  \n",
+       "Samp_1    11.422531  10.862970  \n",
+       "Samp_2    12.413667  10.719110  \n",
+       "Samp_3    11.072915  11.418794  \n",
+       "Samp_4    10.121957  11.039089  \n",
+       "Samp_5    12.206151  10.724849  \n",
+       "...             ...        ...  \n",
+       "Samp_354  11.102427  11.993050  \n",
+       "Samp_355  11.708466  10.654141  \n",
+       "Samp_356  10.830833  10.983455  \n",
+       "Samp_357  11.491449  11.684467  \n",
+       "Samp_358  11.551237  11.221372  \n",
+       "\n",
+       "[358 rows x 100 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>phenotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>235.067423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>253.544991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>234.204994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>281.035429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>245.447781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>236.120451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>222.572359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>268.472285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>235.808167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>213.886123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           phenotype\n",
+       "Samp_1    235.067423\n",
+       "Samp_2    253.544991\n",
+       "Samp_3    234.204994\n",
+       "Samp_4    281.035429\n",
+       "Samp_5    245.447781\n",
+       "...              ...\n",
+       "Samp_354  236.120451\n",
+       "Samp_355  222.572359\n",
+       "Samp_356  268.472285\n",
+       "Samp_357  235.808167\n",
+       "Samp_358  213.886123\n",
+       "\n",
+       "[358 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>Chronic_Bronchitis</th>\n",
+       "      <th>Emphysema</th>\n",
+       "      <th>Asthma</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PatientID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>78</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>68</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>71</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>62</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>61</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31.3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Age  Gender   BMI  Chronic_Bronchitis  Emphysema  Asthma\n",
+       "PatientID                                                          \n",
+       "Samp_1      78       0  31.2                   1          1       0\n",
+       "Samp_2      68       1  19.2                   1          0       0\n",
+       "Samp_3      54       1  19.3                   0          1       1\n",
+       "Samp_4      47       1  36.2                   0          0       1\n",
+       "Samp_5      60       1  26.2                   0          1       1\n",
+       "...        ...     ...   ...                 ...        ...     ...\n",
+       "Samp_354    71       0  23.0                   1          0       1\n",
+       "Samp_355    62       1  25.5                   0          1       1\n",
+       "Samp_356    61       0  21.1                   1          0       0\n",
+       "Samp_357    64       0  37.6                   0          0       1\n",
+       "Samp_358    61       1  31.3                   1          0       0\n",
+       "\n",
+       "[358 rows x 6 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.datasets import DatasetLoader\n",
+    "loader = DatasetLoader(\"example1\")\n",
+    "omics1, omics2, phenotype, clinical = loader.load_data()\n",
+    "\n",
+    "display(omics1)\n",
+    "display(omics2)\n",
+    "display(phenotype)\n",
+    "display(clinical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.external_tools import SmCCNet\n",
+    "merged_omics = pd.concat([omics1, omics2, clinical, phenotype], axis=1)\n",
+    "\n",
+    "smccnet = SmCCNet(\n",
+    "    phenotype_df=phenotype,\n",
+    "    omics_dfs=[omics1, omics2],\n",
+    "    data_types=[\"genes\",\"proteins\"],\n",
+    ")\n",
+    "global_network, smccnet_clusters = smccnet.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(600, 600)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(global_network.shape)\n",
+    "display(len(smccnet_clusters))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from bioneuralnet.network_embedding import GNNEmbedding\n",
+    "\n",
+    "merged_omics = pd.concat([omics1, omics2], axis=1)\n",
+    "\n",
+    "embeddings = GNNEmbedding(\n",
+    "    adjacency_matrix=global_network,\n",
+    "    omics_data=merged_omics,\n",
+    "    phenotype_data=phenotype,\n",
+    "    clinical_data=clinical,\n",
+    "    tune=True,\n",
+    ")\n",
+    "embeddings.fit()\n",
+    "embeddings_output = embeddings.embed(as_df=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(600, 64)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(embeddings_output.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.utils import plot_embeddings\n",
+    "\n",
+    "# Using our embeddings instance we get the necessary labels for the graph.\n",
+    "global_node_labels = embeddings._prepare_node_labels()\n",
+    "embeddings_array = embeddings_output.values  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1 = plot_embeddings(embeddings_array, global_node_labels.to_frame(name=\"phenotype\"), method=\"tsne\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.subject_representation import GraphEmbedding\n",
+    "\n",
+    "enhanced_omics = GraphEmbedding(\n",
+    "    omics_data=merged_omics,\n",
+    "    embeddings=embeddings_output,\n",
+    "    phenotype_data=phenotype,\n",
+    "    tune=True,\n",
+    ")\n",
+    "enhanced_omics_df = enhanced_omics.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene_45</th>\n",
+       "      <th>Gene_197</th>\n",
+       "      <th>Gene_493</th>\n",
+       "      <th>Gene_432</th>\n",
+       "      <th>Gene_357</th>\n",
+       "      <th>Gene_382</th>\n",
+       "      <th>Gene_152</th>\n",
+       "      <th>Gene_427</th>\n",
+       "      <th>Gene_41</th>\n",
+       "      <th>Gene_130</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Gene_202</th>\n",
+       "      <th>Gene_249</th>\n",
+       "      <th>Gene_280</th>\n",
+       "      <th>Gene_62</th>\n",
+       "      <th>Mir_21</th>\n",
+       "      <th>Gene_495</th>\n",
+       "      <th>Mir_59</th>\n",
+       "      <th>Gene_340</th>\n",
+       "      <th>Gene_291</th>\n",
+       "      <th>Gene_81</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>-2.917084</td>\n",
+       "      <td>-13.297334</td>\n",
+       "      <td>-30.571794</td>\n",
+       "      <td>-29.143930</td>\n",
+       "      <td>42.021213</td>\n",
+       "      <td>-12.279085</td>\n",
+       "      <td>-6.925227</td>\n",
+       "      <td>-2.520610</td>\n",
+       "      <td>10.380707</td>\n",
+       "      <td>-6.079539</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.465338</td>\n",
+       "      <td>1.922512</td>\n",
+       "      <td>5.955446</td>\n",
+       "      <td>-2.626381</td>\n",
+       "      <td>13.947213</td>\n",
+       "      <td>-2.806483</td>\n",
+       "      <td>-0.553959</td>\n",
+       "      <td>1.726373</td>\n",
+       "      <td>-2.651651</td>\n",
+       "      <td>-2.406555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>-3.102466</td>\n",
+       "      <td>-12.954265</td>\n",
+       "      <td>-28.690063</td>\n",
+       "      <td>-27.614380</td>\n",
+       "      <td>41.053950</td>\n",
+       "      <td>-12.463960</td>\n",
+       "      <td>-6.747382</td>\n",
+       "      <td>-2.712280</td>\n",
+       "      <td>10.841243</td>\n",
+       "      <td>-6.340821</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.551755</td>\n",
+       "      <td>1.800501</td>\n",
+       "      <td>5.912429</td>\n",
+       "      <td>-2.548239</td>\n",
+       "      <td>12.697251</td>\n",
+       "      <td>-2.648551</td>\n",
+       "      <td>-0.624136</td>\n",
+       "      <td>1.708398</td>\n",
+       "      <td>-2.596500</td>\n",
+       "      <td>-2.342931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>-2.837912</td>\n",
+       "      <td>-13.429165</td>\n",
+       "      <td>-29.601810</td>\n",
+       "      <td>-28.448539</td>\n",
+       "      <td>39.378358</td>\n",
+       "      <td>-12.837721</td>\n",
+       "      <td>-6.436741</td>\n",
+       "      <td>-2.377240</td>\n",
+       "      <td>10.565243</td>\n",
+       "      <td>-6.044507</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.307515</td>\n",
+       "      <td>1.991552</td>\n",
+       "      <td>6.038545</td>\n",
+       "      <td>-2.479833</td>\n",
+       "      <td>13.769784</td>\n",
+       "      <td>-2.652630</td>\n",
+       "      <td>-0.592947</td>\n",
+       "      <td>1.583839</td>\n",
+       "      <td>-2.653498</td>\n",
+       "      <td>-2.446277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>-2.859205</td>\n",
+       "      <td>-12.821326</td>\n",
+       "      <td>-26.844345</td>\n",
+       "      <td>-29.029980</td>\n",
+       "      <td>36.148898</td>\n",
+       "      <td>-12.449477</td>\n",
+       "      <td>-7.229847</td>\n",
+       "      <td>-2.609629</td>\n",
+       "      <td>11.160280</td>\n",
+       "      <td>-6.466545</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.915145</td>\n",
+       "      <td>2.137159</td>\n",
+       "      <td>5.804663</td>\n",
+       "      <td>-2.629407</td>\n",
+       "      <td>13.458064</td>\n",
+       "      <td>-2.546526</td>\n",
+       "      <td>-0.499689</td>\n",
+       "      <td>1.648553</td>\n",
+       "      <td>-2.520416</td>\n",
+       "      <td>-2.530145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>-2.982405</td>\n",
+       "      <td>-12.612553</td>\n",
+       "      <td>-29.984057</td>\n",
+       "      <td>-29.110915</td>\n",
+       "      <td>46.903902</td>\n",
+       "      <td>-12.758489</td>\n",
+       "      <td>-6.868394</td>\n",
+       "      <td>-2.698423</td>\n",
+       "      <td>10.211682</td>\n",
+       "      <td>-6.265595</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.592827</td>\n",
+       "      <td>2.231166</td>\n",
+       "      <td>5.314841</td>\n",
+       "      <td>-2.462155</td>\n",
+       "      <td>12.673634</td>\n",
+       "      <td>-2.597200</td>\n",
+       "      <td>-0.638230</td>\n",
+       "      <td>1.709379</td>\n",
+       "      <td>-2.481453</td>\n",
+       "      <td>-2.397491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>-2.935778</td>\n",
+       "      <td>-13.323266</td>\n",
+       "      <td>-29.435019</td>\n",
+       "      <td>-27.890044</td>\n",
+       "      <td>43.240876</td>\n",
+       "      <td>-12.561521</td>\n",
+       "      <td>-6.723627</td>\n",
+       "      <td>-2.290536</td>\n",
+       "      <td>10.847353</td>\n",
+       "      <td>-6.341079</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.186933</td>\n",
+       "      <td>2.077744</td>\n",
+       "      <td>5.888022</td>\n",
+       "      <td>-2.566317</td>\n",
+       "      <td>13.658852</td>\n",
+       "      <td>-2.439809</td>\n",
+       "      <td>-0.526501</td>\n",
+       "      <td>1.692860</td>\n",
+       "      <td>-2.711183</td>\n",
+       "      <td>-2.466377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>-3.065084</td>\n",
+       "      <td>-13.295135</td>\n",
+       "      <td>-30.718263</td>\n",
+       "      <td>-29.112126</td>\n",
+       "      <td>44.613763</td>\n",
+       "      <td>-12.331960</td>\n",
+       "      <td>-6.955723</td>\n",
+       "      <td>-2.971791</td>\n",
+       "      <td>10.294231</td>\n",
+       "      <td>-6.077254</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.624207</td>\n",
+       "      <td>2.077401</td>\n",
+       "      <td>5.623264</td>\n",
+       "      <td>-2.663072</td>\n",
+       "      <td>14.311062</td>\n",
+       "      <td>-2.595351</td>\n",
+       "      <td>-0.584500</td>\n",
+       "      <td>1.673485</td>\n",
+       "      <td>-2.578159</td>\n",
+       "      <td>-2.418925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>-3.069497</td>\n",
+       "      <td>-14.104972</td>\n",
+       "      <td>-29.862862</td>\n",
+       "      <td>-30.838439</td>\n",
+       "      <td>39.916304</td>\n",
+       "      <td>-12.129115</td>\n",
+       "      <td>-6.739620</td>\n",
+       "      <td>-2.504380</td>\n",
+       "      <td>10.865619</td>\n",
+       "      <td>-6.296479</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.497804</td>\n",
+       "      <td>1.984929</td>\n",
+       "      <td>5.467676</td>\n",
+       "      <td>-2.442813</td>\n",
+       "      <td>14.854524</td>\n",
+       "      <td>-2.540575</td>\n",
+       "      <td>-0.527353</td>\n",
+       "      <td>1.648124</td>\n",
+       "      <td>-2.674988</td>\n",
+       "      <td>-2.482001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>-3.112670</td>\n",
+       "      <td>-14.041763</td>\n",
+       "      <td>-28.716949</td>\n",
+       "      <td>-28.512552</td>\n",
+       "      <td>48.270441</td>\n",
+       "      <td>-12.605819</td>\n",
+       "      <td>-6.424579</td>\n",
+       "      <td>-2.768089</td>\n",
+       "      <td>11.026826</td>\n",
+       "      <td>-6.542514</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.566136</td>\n",
+       "      <td>2.141326</td>\n",
+       "      <td>6.178911</td>\n",
+       "      <td>-2.667312</td>\n",
+       "      <td>13.193234</td>\n",
+       "      <td>-2.821949</td>\n",
+       "      <td>-0.585463</td>\n",
+       "      <td>1.659515</td>\n",
+       "      <td>-2.625582</td>\n",
+       "      <td>-2.425626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>-2.983813</td>\n",
+       "      <td>-13.657581</td>\n",
+       "      <td>-28.264297</td>\n",
+       "      <td>-28.283759</td>\n",
+       "      <td>41.077029</td>\n",
+       "      <td>-12.508963</td>\n",
+       "      <td>-6.906523</td>\n",
+       "      <td>-2.443945</td>\n",
+       "      <td>11.551972</td>\n",
+       "      <td>-6.444730</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.345094</td>\n",
+       "      <td>2.132126</td>\n",
+       "      <td>5.578996</td>\n",
+       "      <td>-2.523854</td>\n",
+       "      <td>14.942860</td>\n",
+       "      <td>-2.537228</td>\n",
+       "      <td>-0.534264</td>\n",
+       "      <td>1.689256</td>\n",
+       "      <td>-2.751487</td>\n",
+       "      <td>-2.255327</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 600 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Gene_45   Gene_197   Gene_493   Gene_432   Gene_357   Gene_382  \\\n",
+       "Samp_1   -2.917084 -13.297334 -30.571794 -29.143930  42.021213 -12.279085   \n",
+       "Samp_2   -3.102466 -12.954265 -28.690063 -27.614380  41.053950 -12.463960   \n",
+       "Samp_3   -2.837912 -13.429165 -29.601810 -28.448539  39.378358 -12.837721   \n",
+       "Samp_4   -2.859205 -12.821326 -26.844345 -29.029980  36.148898 -12.449477   \n",
+       "Samp_5   -2.982405 -12.612553 -29.984057 -29.110915  46.903902 -12.758489   \n",
+       "...            ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354 -2.935778 -13.323266 -29.435019 -27.890044  43.240876 -12.561521   \n",
+       "Samp_355 -3.065084 -13.295135 -30.718263 -29.112126  44.613763 -12.331960   \n",
+       "Samp_356 -3.069497 -14.104972 -29.862862 -30.838439  39.916304 -12.129115   \n",
+       "Samp_357 -3.112670 -14.041763 -28.716949 -28.512552  48.270441 -12.605819   \n",
+       "Samp_358 -2.983813 -13.657581 -28.264297 -28.283759  41.077029 -12.508963   \n",
+       "\n",
+       "          Gene_152  Gene_427    Gene_41  Gene_130  ...   Gene_202  Gene_249  \\\n",
+       "Samp_1   -6.925227 -2.520610  10.380707 -6.079539  ...  10.465338  1.922512   \n",
+       "Samp_2   -6.747382 -2.712280  10.841243 -6.340821  ...  10.551755  1.800501   \n",
+       "Samp_3   -6.436741 -2.377240  10.565243 -6.044507  ...  10.307515  1.991552   \n",
+       "Samp_4   -7.229847 -2.609629  11.160280 -6.466545  ...   9.915145  2.137159   \n",
+       "Samp_5   -6.868394 -2.698423  10.211682 -6.265595  ...  10.592827  2.231166   \n",
+       "...            ...       ...        ...       ...  ...        ...       ...   \n",
+       "Samp_354 -6.723627 -2.290536  10.847353 -6.341079  ...  10.186933  2.077744   \n",
+       "Samp_355 -6.955723 -2.971791  10.294231 -6.077254  ...   9.624207  2.077401   \n",
+       "Samp_356 -6.739620 -2.504380  10.865619 -6.296479  ...  10.497804  1.984929   \n",
+       "Samp_357 -6.424579 -2.768089  11.026826 -6.542514  ...  10.566136  2.141326   \n",
+       "Samp_358 -6.906523 -2.443945  11.551972 -6.444730  ...  10.345094  2.132126   \n",
+       "\n",
+       "          Gene_280   Gene_62     Mir_21  Gene_495    Mir_59  Gene_340  \\\n",
+       "Samp_1    5.955446 -2.626381  13.947213 -2.806483 -0.553959  1.726373   \n",
+       "Samp_2    5.912429 -2.548239  12.697251 -2.648551 -0.624136  1.708398   \n",
+       "Samp_3    6.038545 -2.479833  13.769784 -2.652630 -0.592947  1.583839   \n",
+       "Samp_4    5.804663 -2.629407  13.458064 -2.546526 -0.499689  1.648553   \n",
+       "Samp_5    5.314841 -2.462155  12.673634 -2.597200 -0.638230  1.709379   \n",
+       "...            ...       ...        ...       ...       ...       ...   \n",
+       "Samp_354  5.888022 -2.566317  13.658852 -2.439809 -0.526501  1.692860   \n",
+       "Samp_355  5.623264 -2.663072  14.311062 -2.595351 -0.584500  1.673485   \n",
+       "Samp_356  5.467676 -2.442813  14.854524 -2.540575 -0.527353  1.648124   \n",
+       "Samp_357  6.178911 -2.667312  13.193234 -2.821949 -0.585463  1.659515   \n",
+       "Samp_358  5.578996 -2.523854  14.942860 -2.537228 -0.534264  1.689256   \n",
+       "\n",
+       "          Gene_291   Gene_81  \n",
+       "Samp_1   -2.651651 -2.406555  \n",
+       "Samp_2   -2.596500 -2.342931  \n",
+       "Samp_3   -2.653498 -2.446277  \n",
+       "Samp_4   -2.520416 -2.530145  \n",
+       "Samp_5   -2.481453 -2.397491  \n",
+       "...            ...       ...  \n",
+       "Samp_354 -2.711183 -2.466377  \n",
+       "Samp_355 -2.578159 -2.418925  \n",
+       "Samp_356 -2.674988 -2.482001  \n",
+       "Samp_357 -2.625582 -2.425626  \n",
+       "Samp_358 -2.751487 -2.255327  \n",
+       "\n",
+       "[358 rows x 600 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(enhanced_omics_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.utils import evaluate_rf_regressor\n",
+    "from bioneuralnet.utils import plot_performance\n",
+    "\n",
+    "y_phenotype = phenotype.values\n",
+    "X_enriched_omics = enhanced_omics_df.values\n",
+    "X_raw_omics = merged_omics.values\n",
+    "\n",
+    "accuracy_with_embeddings = evaluate_rf_regressor(X_enriched_omics, y_phenotype)\n",
+    "accuracy_alone = evaluate_rf_regressor(X_enriched_omics, y_phenotype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_performance(accuracy_with_embeddings, accuracy_alone, \"Raw Omics vs Enriched Omics\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Mir_2</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gene_1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gene_2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Gene_6</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Gene_7</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Omic  Degree\n",
+       "Index                \n",
+       "15      Mir_2      14\n",
+       "1      Gene_1       4\n",
+       "2      Gene_2       4\n",
+       "3      Gene_6       4\n",
+       "4      Gene_7       4"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Mir_53</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gene_5</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gene_54</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Gene_88</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Gene_174</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Omic  Degree\n",
+       "Index                  \n",
+       "7        Mir_53       6\n",
+       "1        Gene_5       4\n",
+       "2       Gene_54       4\n",
+       "4       Gene_88       3\n",
+       "5      Gene_174       3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Lets take a quick look at some of those clusters from SmCCNet\n",
+    "from bioneuralnet.utils import plot_network\n",
+    "\n",
+    "cluster1, cluster2, cluster3 = smccnet_clusters\n",
+    "\n",
+    "cluster1_mapping = plot_network(cluster2, weight_threshold=0.1, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster1_mapping.head())\n",
+    "\n",
+    "cluster2_mapping = plot_network(cluster1, weight_threshold=0.009, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster2_mapping.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.clustering import CorrelatedLouvain\n",
+    "import networkx as nx\n",
+    "\n",
+    "merged_omics = pd.concat([omics1, omics2], axis=1)\n",
+    "G_network = nx.from_pandas_adjacency(global_network)\n",
+    "\n",
+    "louvain_instance = CorrelatedLouvain(\n",
+    "    G=G_network,\n",
+    "    B=merged_omics,\n",
+    "    Y=phenotype,\n",
+    "    tune=True\n",
+    ")\n",
+    "\n",
+    "louvain_clusters = louvain_instance.run(as_dfs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(louvain_clusters))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cluster</th>\n",
+       "      <th>Louvain Size</th>\n",
+       "      <th>Louvain Correlation</th>\n",
+       "      <th>SMCCNET Size</th>\n",
+       "      <th>SMCCNET Correlation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cluster_1</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0.724029</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.134853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cluster_2</td>\n",
+       "      <td>76</td>\n",
+       "      <td>-0.124891</td>\n",
+       "      <td>63</td>\n",
+       "      <td>0.731769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cluster_3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.069064</td>\n",
+       "      <td>15</td>\n",
+       "      <td>-0.085057</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Cluster  Louvain Size  Louvain Correlation  SMCCNET Size  \\\n",
+       "0  Cluster_1            77             0.724029            67   \n",
+       "1  Cluster_2            76            -0.124891            63   \n",
+       "2  Cluster_3             6             0.069064            15   \n",
+       "\n",
+       "   SMCCNET Correlation  \n",
+       "0             0.134853  \n",
+       "1             0.731769  \n",
+       "2            -0.085057  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets comapre these clusters against SmCCNet clusters\n",
+    "from bioneuralnet.utils import compare_clusters\n",
+    "\n",
+    "compare_clusters(louvain_clusters, smccnet_clusters, phenotype, merged_omics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Gene_222</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Mir_2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>Gene_1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Gene_6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Gene_7</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Omic  Degree\n",
+       "Index                  \n",
+       "34     Gene_222       7\n",
+       "30        Mir_2       7\n",
+       "66       Gene_1       7\n",
+       "45       Gene_6       6\n",
+       "53       Gene_7       6"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Gene_187</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Gene_464</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Gene_83</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Gene_354</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Gene_300</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Omic  Degree\n",
+       "Index                  \n",
+       "30     Gene_187       9\n",
+       "44     Gene_464       6\n",
+       "21      Gene_83       6\n",
+       "16     Gene_354       6\n",
+       "9      Gene_300       5"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.utils import plot_network\n",
+    "from bioneuralnet.metrics import convert_louvain_to_adjacency\n",
+    "\n",
+    "cluster1 = louvain_clusters[0]\n",
+    "cluster2 = louvain_clusters[1]\n",
+    "\n",
+    "# Convert Louvain clusters into adjacency matrices\n",
+    "louvain_adj1 = convert_louvain_to_adjacency(cluster1)\n",
+    "louvain_adj2 = convert_louvain_to_adjacency(cluster2)\n",
+    "\n",
+    "# Plot using the converted adjacency matrices\n",
+    "cluster1_mapping = plot_network(louvain_adj1, weight_threshold=0.1, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster1_mapping.head())\n",
+    "\n",
+    "cluster2_mapping = plot_network(louvain_adj2, weight_threshold=0.1, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster2_mapping.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>phenotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>235.067423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>253.544991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>234.204994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>281.035429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>245.447781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>236.120451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>222.572359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>268.472285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>235.808167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>213.886123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           phenotype\n",
+       "Samp_1    235.067423\n",
+       "Samp_2    253.544991\n",
+       "Samp_3    234.204994\n",
+       "Samp_4    281.035429\n",
+       "Samp_5    245.447781\n",
+       "...              ...\n",
+       "Samp_354  236.120451\n",
+       "Samp_355  222.572359\n",
+       "Samp_356  268.472285\n",
+       "Samp_357  235.808167\n",
+       "Samp_358  213.886123\n",
+       "\n",
+       "[358 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>phenotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         phenotype\n",
+       "Samp_1           1\n",
+       "Samp_2           2\n",
+       "Samp_3           1\n",
+       "Samp_4           3\n",
+       "Samp_5           2\n",
+       "...            ...\n",
+       "Samp_354         1\n",
+       "Samp_355         1\n",
+       "Samp_356         2\n",
+       "Samp_357         1\n",
+       "Samp_358         1\n",
+       "\n",
+       "[358 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "phenotype\n",
+       "0     38\n",
+       "1    158\n",
+       "2    141\n",
+       "3     21\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.datasets import DatasetLoader\n",
+    "import numpy as np\n",
+    "\n",
+    "loader = DatasetLoader(\"example1\")\n",
+    "omics1, omics2, phenotype, clinical = loader.load_data()\n",
+    "display(phenotype)\n",
+    "\n",
+    "min_val = phenotype[\"phenotype\"].min()\n",
+    "max_val = phenotype[\"phenotype\"].max()\n",
+    "\n",
+    "# linspace creates an array of evenly spaced values\n",
+    "bins = np.linspace(min_val, max_val, 5)\n",
+    "\n",
+    "phenotype[\"phenotype\"] = pd.cut(phenotype[\"phenotype\"], bins=bins, labels=[0, 1, 2, 3], include_lowest=True)\n",
+    "count_values = phenotype[\"phenotype\"].value_counts(sort=False)\n",
+    "\n",
+    "display(phenotype)\n",
+    "display(count_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DPMON (Disease Prediction using Multi-Omics Networks) reuses the same GNN architectures but with a different objective: \n",
+    "- Instead of node-level MSE regression, DPMON aggregates node embeddings with patient-level omics data. \n",
+    "- A downstream classification head (e.g., softmax layer with CrossEntropyLoss) is applied for sample-level disease prediction. \n",
+    "- This end-to-end approach leverages both local (node-level) and global (patient-level) network information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.downstream_task import DPMON\n",
+    "dpmon = DPMON(\n",
+    "    adjacency_matrix=global_network,\n",
+    "    omics_list=[omics1, omics2],\n",
+    "    phenotype_data=phenotype,\n",
+    "    clinical_data=clinical,\n",
+    "    tune=True,\n",
+    ")\n",
+    "dpmon_predictions = dpmon.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual</th>\n",
+       "      <th>Predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Actual  Predicted\n",
+       "0        1          1\n",
+       "1        2          2\n",
+       "2        1          1\n",
+       "3        3          3\n",
+       "4        2          2\n",
+       "..     ...        ...\n",
+       "353      1          1\n",
+       "354      1          1\n",
+       "355      2          2\n",
+       "356      1          1\n",
+       "357      1          1\n",
+       "\n",
+       "[358 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(dpmon_predictions[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.utils import evaluate_rf_classifier\n",
+    "\n",
+    "X_raw = merged_omics.values\n",
+    "y_global = phenotype.values\n",
+    "raw_rf_acc = evaluate_rf_classifier(X_raw, y_global)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.utils import plot_performance\n",
+    "plot_performance(dpmon_predictions[1], raw_rf_acc, \"Raw Omics, vs DPMON Omics\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".test_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assets/enchaned_w_omics.png b/assets/enchaned_w_omics.png
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literal 0
HcmV?d00001

diff --git a/bioneuralnet/__init__.py b/bioneuralnet/__init__.py
index b0fed8c..0d0da00 100644
--- a/bioneuralnet/__init__.py
+++ b/bioneuralnet/__init__.py
@@ -6,38 +6,36 @@
 Key Features:
     - **Network Embedding**: Generate lower-dimensional representations using Graph Neural Networks (GNNs).
     - **Subject Representation**: Combine network-derived embeddings with raw omics data to produce enriched subject-level profiles.
-    - **Clustering & Analysis**: Leverage clustering algorithms and feature selection methods to identify functional modules and informative biomarkers.
+    - **Correlated Clustering**: BioNeuralNet includes internal modules for performing correlated clustering on complex networks to identify functional modules and informative biomarkers.
     - **Downstream Prediction**: Execute end-to-end pipelines (DPMON) for disease phenotype prediction using network information.
-    - **External Integration**: Easily interface with external tools (WGCNA, SmCCNet, Node2Vec, among others.) for network
-      construction, visualization, and advanced analysis.
+    - **External Integration**: Easily interface with external tools (WGCNA, SmCCNet, Node2Vec, among others.) for network construction, visualization, and advanced analysis.
 
 Modules:
     - `network_embedding`: Generates network embeddings via GNNs and Node2Vec.
     - `subject_representation`: Integrates network embeddings into omics data.
     - `downstream_task`: Contains pipelines for disease prediction (e.g., DPMON).
-    - `analysis`: Provides tools for feature selection and clustering.
-    - `external_tools`: Wraps external packages (e.g., WGCNA, SmCCNet, Node2Vec, FeatureSelector, StaticVisualizer, DynamicVisualizer, HierarchicalClustering) for quick integration.
-    - `utils`: Utilities for configuration, logging, file handling, and more.
-
+    - `metrics`: Provides tools for checking the variance of a dataset and computing correlation.
+    - `external_tools`: Wraps external packages (e.g.WGCNA and SmCCNet) for quick integration.
+    - `utils`: Utilities for configuration, logging, file handling, converting .Rdata files to csv and more.
+    - `datasets`: Contains example (synthetic) datasets for testing and demonstration purposes.
 """
 
-__version__: str = "0.1.0"
+__version__ = "0.2.0b2"
 
 from .network_embedding import GNNEmbedding
 from .subject_representation import GraphEmbedding
 from .downstream_task import DPMON
-from .clustering import PageRank
-from .clustering import Louvain
-from .metrics import correlation
-from .metrics import mse
-from .metrics import f1_score
-from .metrics import precision
-from .metrics import recall
+from .clustering import CorrelatedPageRank
+from .clustering import CorrelatedLouvain
+from .clustering import HybridLouvain
+from .metrics import compute_cluster_correlation_from_df, compute_correlation, convert_louvain_to_adjacency
+from .metrics import CheckVariance, network_remove_low_variance, network_remove_high_zero_fraction
+
+from .datasets import DatasetLoader
+from .utils import get_logger
+from .utils import rdata_to_csv_file
+from .utils import evaluate_rf_regressor,evaluate_rf_classifier,plot_embeddings, plot_network, plot_performance,compare_clusters
 
-from .external_tools import DynamicVisualizer
-from .external_tools import FeatureSelector
-from .external_tools import HierarchicalClustering
-from .external_tools import StaticVisualizer
 from .external_tools import SmCCNet
 from .external_tools import WGCNA
 from .external_tools import Node2Vec
@@ -48,18 +46,30 @@
     "GraphEmbedding",
     "DPMON",
     "PageRank",
-    "Louvain",
+    "CorrelatedPageRank",
+    "CorrelatedLouvain",
     "correlation",
-    "mse",
-    "f1_score",
-    "precision",
-    "recall",
+    "logger",
+    "Correlation",
+    "HybridLouvain",
+    "CheckVariance",
     "utils",
     "Node2Vec",
-    "FeatureSelector",
-    "StaticVisualizer",
-    "DynamicVisualizer",
-    "HierarchicalClustering",
+    "get_logger",
+    "rdata_to_csv_file",
+    "evaluate_rf",
+    "plot_embeddings",
+    "plot_network",
+    "plot_performance",
+    "compute_cluster_correlation_from_df",
+    "compute_correlation",
+    "network_remove_low_variance",
+    "network_remove_high_zero_fraction",
     "SmCCNet",
     "WGCNA",
+    "DatasetLoader",
+    "compare_clusters",
+    "evaluate_rf_regressor",
+    "evaluate_rf_classifier",
+    "convert_louvain_to_adjacency"
 ]
diff --git a/bioneuralnet/clustering/correlated_louvain.py b/bioneuralnet/clustering/correlated_louvain.py
index fdb8f8e..73b72ed 100644
--- a/bioneuralnet/clustering/correlated_louvain.py
+++ b/bioneuralnet/clustering/correlated_louvain.py
@@ -1,6 +1,7 @@
 import numpy as np
 import networkx as nx
 import pandas as pd
+from typing import Union
 from community.community_louvain import (
     modularity as original_modularity,
     best_partition,
@@ -126,20 +127,79 @@ def _quality_correlated(self, partition) -> float:
         )
         return quality
 
-    def run(self) -> dict:
-        if self.tune:
+    def run(self, as_dfs: bool = False) -> Union[dict, list]:
+        """
+        Run correlated Louvain clustering.
+
+        If as_dfs is True, returns a list of adjacency matrices (DataFrames),
+        where each adjacency matrix represents a cluster with more than 2 nodes.
+        Otherwise, returns the partition dictionary.
+
+        If tune is True and as_dfs is False, hyperparameter tuning is performed and the best parameters are returned.
+        If tune is True and as_dfs is True, tuning is performed, and then standard detection is run using the tuned parameters.
+        """
+        if self.tune and not as_dfs:
             self.logger.info("Tuning enabled. Running hyperparameter tuning...")
-            best_config = self.run_tuning(num_samples=5)
+            best_config = self.run_tuning(num_samples=10)
             self.logger.info("Tuning completed successfully.")
             return {"best_config": best_config}
+
+        elif self.tune and as_dfs:
+            self.logger.info("Tuning enabled and adjacency matrices output requested.")
+            best_config = self.run_tuning(num_samples=10)
+            tuned_k4 = best_config.get("k4", 0.8)
+            tuned_k3 = 1.0 - tuned_k4
+            tuned_instance = CorrelatedLouvain(
+                G=self.G,
+                B=self.B,
+                Y=self.Y,
+                k3=tuned_k3,
+                k4=tuned_k4,
+                weight=self.weight,
+                tune=False
+            )
+            return tuned_instance.run(as_dfs=True)
+
         else:
             self.logger.info("Running standard community detection...")
             partition = best_partition(self.G, weight=self.weight)
             quality = self._quality_correlated(partition)
             self.logger.info(f"Final quality: {quality:.4f}")
             self.partition = partition
+
+        if as_dfs:
+            self.logger.info("Raw partition output:", self.partition)
+            clusters_dfs = self.partition_to_adjacency(self.partition)
+            print(f"Returning {len(clusters_dfs)} clusters after filtering")
+            return clusters_dfs
+
+        else:
             return partition
 
+    def partition_to_adjacency(self, partition: dict) -> list:
+        """
+        Convert the partition dictionary into a list of adjacency matrices (DataFrames),
+        where each adjacency matrix represents a cluster with more than 2 nodes.
+        """
+        clusters = {}
+        for node, cl in partition.items():
+            clusters.setdefault(cl, []).append(node)
+
+        self.logger.debug(f"Total detected clusters: {len(clusters)}")
+
+        adjacency_matrices = []
+        for cl, nodes in clusters.items():
+            self.logger.debug(f"Cluster {cl} size: {len(nodes)}")  
+            if len(nodes) > 2: 
+                valid_nodes = list(set(nodes).intersection(set(self.B.columns)))  
+                if valid_nodes:
+                    adjacency_matrix = self.B.loc[:, valid_nodes].fillna(0)
+                    adjacency_matrices.append(adjacency_matrix)
+
+        print(f"Clusters with >2 nodes: {len(adjacency_matrices)}")
+        
+        return adjacency_matrices
+
     def get_quality(self) -> float:
         if not hasattr(self, "partition"):
             raise ValueError("No partition computed. Call run() first.")
@@ -177,6 +237,7 @@ def short_dirname_creator(trial):
         analysis = tune.run(
             tune.with_parameters(self._tune_helper),
             config=search_config,
+            verbose=0,
             num_samples=num_samples,
             scheduler=scheduler,
             progress_reporter=reporter,
diff --git a/bioneuralnet/clustering/correlated_pagerank.py b/bioneuralnet/clustering/correlated_pagerank.py
index fc5ba5d..d725c40 100644
--- a/bioneuralnet/clustering/correlated_pagerank.py
+++ b/bioneuralnet/clustering/correlated_pagerank.py
@@ -427,6 +427,7 @@ def short_dirname_creator(trial):
         analysis = tune.run(
             tune.with_parameters(self._tune_helper),
             config=search_config,
+            verbose=0,
             num_samples=num_samples,
             scheduler=scheduler,
             progress_reporter=reporter,
diff --git a/bioneuralnet/clustering/hybrid_louvain.py b/bioneuralnet/clustering/hybrid_louvain.py
index c108711..208dbae 100644
--- a/bioneuralnet/clustering/hybrid_louvain.py
+++ b/bioneuralnet/clustering/hybrid_louvain.py
@@ -164,123 +164,3 @@ def run(self) -> dict:
         self.logger.info(f"Hybrid Louvain completed after {iteration+1} iterations.")
         return {"curr": current_partition, "clus": all_clusters}
 
-
-## Previous implementation before introducing hyperparameter tuning
-
-
-# import networkx as nx
-# import pandas as pd
-# import numpy as np
-
-# from bioneuralnet.clustering.correlated_pagerank import CorrelatedPageRank
-# from bioneuralnet.clustering.correlated_louvain import CorrelatedLouvain
-
-# from ..utils.logger import get_logger
-
-
-# logger = get_logger(__name__)
-
-
-# class HybridLouvain:
-#     def __init__(
-#         self,
-#         G: nx.Graph,
-#         B: pd.DataFrame,
-#         Y,
-#         k3: float = 0.2,
-#         k4: float = 0.8,
-#         max_iter: int = 10,
-#         weight: str = "weight",
-#         tune: bool = False,
-#     ):
-#         self.logger = get_logger(__name__)
-#         self.G = G
-#         self.B = B
-#         self.Y = Y
-#         self.k3 = k3
-#         self.k4 = k4
-#         self.weight = weight
-#         self.max_iter = max_iter
-#         self.tune = tune
-#         self.logger.info(
-#             f"Initialized HybridLouvain with max_iter={self.max_iter}, k3={self.k3}, k4={self.k4}"
-#         )
-
-#     def run(self) -> dict:
-#         iteration = 0
-#         prev_size = len(self.G.nodes())
-#         current_partition = None
-
-#         all_clusters = {}
-
-#         while iteration < self.max_iter:
-#             self.logger.info(
-#                 f"\nIteration {iteration+1}/{self.max_iter}: Running Correlated Louvain..."
-#             )
-#             clouvain = CorrelatedLouvain(
-#                 self.G,
-#                 B=self.B,
-#                 Y=self.Y,
-#                 k3=self.k3,
-#                 k4=self.k4,
-#                 weight=self.weight,
-#                 tune=self.tune,
-#             )
-#             partition = clouvain.run()
-#             quality_val = clouvain.get_quality()
-
-#             self.logger.info(f"Iteration {iteration+1}: Quality = {quality_val:.4f}")
-#             current_partition = partition
-
-#             best_corr = 0
-#             best_seed = None
-#             # loop over communities in the partition to find the best seed community.
-#             for com in set(partition.values()):
-#                 nodes = [n for n in self.G.nodes() if partition[n] == com]
-#                 if len(nodes) < 2:
-#                     continue
-#                 try:
-#                     corr, _ = clouvain._compute_community_correlation(nodes)
-#                     if abs(corr) > abs(best_corr):
-#                         best_corr = corr
-#                         best_seed = nodes
-#                 except Exception as e:
-#                     self.logger.info(
-#                         f"Error computing correlation for community {com}: {e}"
-#                     )
-#             if best_seed is None:
-#                 self.logger.info("No valid seed community found; stopping iterations.")
-#                 break
-#             self.logger.info(
-#                 f"Selected seed community of size {len(best_seed)} with correlation {best_corr:.4f}"
-#             )
-
-#             self.logger.info(
-#                 "Running Correlated PageRank on selected seed community..."
-#             )
-#             pagerank_instance = CorrelatedPageRank(
-#                 graph=self.G,
-#                 omics_data=self.B,
-#                 phenotype_data=self.Y,
-#                 tune=self.tune,
-#             )
-#             pagerank_results = pagerank_instance.run(best_seed)
-#             refined_nodes = pagerank_results.get("cluster_nodes", [])
-
-#             new_size = len(refined_nodes)
-#             all_clusters[iteration] = refined_nodes
-#             self.logger.info(f"Refined subgraph size: {new_size}")
-#             if new_size == prev_size:
-#                 self.logger.info("Subgraph size converged. Stopping iterations.")
-#                 break
-#             elif new_size <= 1:
-#                 self.logger.info(
-#                     "Refined subgraph contains only one node. Stopping iterations."
-#                 )
-#                 break
-#             prev_size = new_size
-#             self.G = self.G.subgraph(refined_nodes).copy()
-#             iteration += 1
-
-#         self.logger.info(f"Hybrid Louvain completed after {iteration+1} iterations.")
-#         return {"curr": current_partition, "clus": all_clusters}
diff --git a/bioneuralnet/datasets/dataset_loader.py b/bioneuralnet/datasets/dataset_loader.py
index 616da1c..ab5b049 100644
--- a/bioneuralnet/datasets/dataset_loader.py
+++ b/bioneuralnet/datasets/dataset_loader.py
@@ -1,51 +1,68 @@
 import os
 import pandas as pd
-from typing import Tuple, Optional
-
 
 class DatasetLoader:
-    """
-    This class allows users to load predefined sample datasets by specifying
-    the dataset name. Each dataset should contain three CSV files:
-    - omics1.csv: Genomic features
-    - omics2.csv: miRNA features
-    - pheno.csv: Phenotype data with a single 'Pheno' column representing the target outcome
-    """
-
-    def __init__(self, base_dir: Optional[str] = None):
+    def __init__(self, dataset_name: str):
         """
-        Initializes the DatasetLoader with the base data directory.
+        Initializes the loader with the dataset name.
+        
+        Args:
+            dataset_name (str): Either "example1" or "example2".
         """
-        if base_dir is None:
-            self.base_dir = os.path.join(os.path.dirname(__file__), "example1")
-        else:
-            self.base_dir = base_dir
-
-    def __call__(self, name: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        self.dataset_name = dataset_name.strip().lower()
+        self.base_dir = os.path.dirname(__file__)
+    
+    def load_data(self):
         """
-        Loads the specified dataset.
-        Args: name (str): Name of the dataset to load (corresponds to a subdirectory in the datasets folder).
-        Returns: tuple: A tuple containing the omics1, omics2, and pheno DataFrames.
-        Raises: FileNotFoundError if the required files are not found.
+        Loads the dataset and returns a tuple of four DataFrames.
+        
+        Returns:
+            tuple: 
+              - For "example1": (omics1, omics2, pheno, clinical)
+              - For "example2": (gene_data, mirna_data, rppa_data, clinical_data)
+        
+        Raises:
+            FileNotFoundError: If any required file is missing.
+            ValueError: If the dataset name is not valid.
         """
-        dataset_path = os.path.join(os.path.dirname(__file__), name)
-        if not os.path.isdir(dataset_path):
-            raise ValueError(
-                f"Dataset '{name}' not found in '{os.path.dirname(__file__)}'."
-            )
+        if self.dataset_name == "example2":
+            dataset_path = os.path.join(self.base_dir, "example2")
+            gene_file     = os.path.join(dataset_path, "gene_data.csv")
+            mirna_file    = os.path.join(dataset_path, "mirna_data.csv")
+            rppa_file     = os.path.join(dataset_path, "rppa_data.csv")
+            clinical_file = os.path.join(dataset_path, "clinical_data.csv")
+            
+            for f in [gene_file, mirna_file, rppa_file, clinical_file]:
+                if not os.path.isfile(f):
+                    raise FileNotFoundError(
+                        f"Required file '{os.path.basename(f)}' not found in '{dataset_path}'."
+                    )
+            
+            gene_data    = pd.read_csv(gene_file)
+            mirna_data   = pd.read_csv(mirna_file)
+            rppa_data    = pd.read_csv(rppa_file)
+            clinical_data = pd.read_csv(clinical_file)
+            
+            return gene_data, mirna_data, rppa_data, clinical_data
+        
+        elif self.dataset_name == "example1":
+            dataset_path = os.path.join(self.base_dir, "example1")
+            x1_file      = os.path.join(dataset_path, "X1.csv")
+            x2_file      = os.path.join(dataset_path, "X2.csv")
+            y_file       = os.path.join(dataset_path, "Y.csv")
+            clinical_file = os.path.join(dataset_path, "clinical_data.csv")
 
-        omics1_path = os.path.join(dataset_path, "X1.csv")
-        omics2_path = os.path.join(dataset_path, "X2.csv")
-        pheno_path = os.path.join(dataset_path, "Y.csv")
-
-        for path in [omics1_path, omics2_path, pheno_path]:
-            if not os.path.isfile(path):
-                raise FileNotFoundError(
-                    f"Required file '{os.path.basename(path)}' not found in '{dataset_path}'."
-                )
-
-        omics1 = pd.read_csv(omics1_path, index_col=0)
-        omics2 = pd.read_csv(omics2_path, index_col=0)
-        pheno = pd.read_csv(pheno_path, index_col=0)
-
-        return omics1, omics2, pheno
+            for f in [x1_file, x2_file, y_file, clinical_file]:
+                if not os.path.isfile(f):
+                    raise FileNotFoundError(
+                        f"Required file '{os.path.basename(f)}' not found in '{dataset_path}'."
+                    )
+            
+            omics1 = pd.read_csv(x1_file, index_col=0)
+            omics2 = pd.read_csv(x2_file, index_col=0)
+            pheno  = pd.read_csv(y_file, index_col=0)
+            clinical = pd.read_csv(clinical_file, index_col=0)  
+            return omics1, omics2, pheno, clinical
+        
+        else:
+            raise ValueError("Dataset name must be either 'example1' or 'example2'.")
diff --git a/bioneuralnet/datasets/example1/Y.csv b/bioneuralnet/datasets/example1/Y.csv
index f96ba98..985d42b 100644
--- a/bioneuralnet/datasets/example1/Y.csv
+++ b/bioneuralnet/datasets/example1/Y.csv
@@ -1,4 +1,4 @@
-"","Pheno"
+"","phenotype"
 "Samp_1",235.06742273556
 "Samp_2",253.544991042769
 "Samp_3",234.204994225565
diff --git a/bioneuralnet/datasets/example1/clinical_data.csv b/bioneuralnet/datasets/example1/clinical_data.csv
new file mode 100644
index 0000000..7d631ff
--- /dev/null
+++ b/bioneuralnet/datasets/example1/clinical_data.csv
@@ -0,0 +1,359 @@
+PatientID,Age,Gender,BMI,Chronic_Bronchitis,Emphysema,Asthma
+Samp_1,78,0,31.2,1,1,0
+Samp_2,68,1,19.2,1,0,0
+Samp_3,54,1,19.3,0,1,1
+Samp_4,47,1,36.2,0,0,1
+Samp_5,60,1,26.2,0,1,1
+Samp_6,78,1,21.2,1,0,1
+Samp_7,58,1,29.7,0,0,0
+Samp_8,62,1,35.1,1,1,1
+Samp_9,50,0,23.1,0,0,0
+Samp_10,50,1,31.9,1,0,1
+Samp_11,63,0,20.3,1,1,0
+Samp_12,75,1,19.6,0,0,0
+Samp_13,79,1,29.9,1,0,1
+Samp_14,63,0,30.1,1,1,0
+Samp_15,42,1,32.2,0,1,0
+Samp_16,61,1,34.1,1,0,1
+Samp_17,41,0,39.5,0,0,0
+Samp_18,63,1,29.6,1,1,1
+Samp_19,69,1,25.4,0,1,1
+Samp_20,77,1,35.6,0,0,1
+Samp_21,41,0,24.3,1,1,0
+Samp_22,60,1,27.9,1,0,0
+Samp_23,72,1,20.2,1,0,0
+Samp_24,51,1,19,1,0,1
+Samp_25,61,1,39.2,1,0,0
+Samp_26,64,0,36.5,0,0,1
+Samp_27,66,0,33.5,1,1,1
+Samp_28,67,1,27.3,0,1,0
+Samp_29,55,1,22.2,0,1,1
+Samp_30,54,0,21.9,1,1,0
+Samp_31,42,0,23.9,0,0,0
+Samp_32,76,1,30.3,0,0,0
+Samp_33,46,0,33.9,0,0,0
+Samp_34,60,1,32.7,0,0,0
+Samp_35,48,0,24.5,1,1,1
+Samp_36,78,0,39,0,0,1
+Samp_37,57,0,34.4,1,0,1
+Samp_38,43,0,30.4,1,1,0
+Samp_39,64,1,31.7,0,1,0
+Samp_40,53,0,27.5,1,1,0
+Samp_41,48,0,23.8,1,1,1
+Samp_42,65,1,26.2,1,0,0
+Samp_43,41,0,34.8,0,0,1
+Samp_44,59,0,18.8,1,1,1
+Samp_45,67,1,21,1,0,0
+Samp_46,46,0,19.5,1,0,0
+Samp_47,47,1,19.4,0,0,1
+Samp_48,74,1,36.9,1,1,0
+Samp_49,53,1,33.6,0,0,1
+Samp_50,56,1,28.7,0,1,0
+Samp_51,75,0,20.6,0,0,0
+Samp_52,79,1,29.1,0,0,1
+Samp_53,43,0,28.7,0,0,0
+Samp_54,41,0,22.2,0,1,1
+Samp_55,45,0,27.8,1,0,0
+Samp_56,43,0,27.1,0,1,1
+Samp_57,68,0,31.7,1,1,1
+Samp_58,57,0,32.2,1,1,0
+Samp_59,65,0,19.5,0,0,1
+Samp_60,73,0,26.6,1,0,0
+Samp_61,49,0,32,1,0,0
+Samp_62,75,0,29.3,1,0,0
+Samp_63,53,1,36.9,1,1,1
+Samp_64,70,1,32.7,0,1,1
+Samp_65,54,1,22,1,1,0
+Samp_66,47,0,20,0,0,0
+Samp_67,53,1,32.3,1,1,1
+Samp_68,62,1,19.1,1,0,0
+Samp_69,79,1,31.1,0,1,0
+Samp_70,60,0,38.7,1,0,1
+Samp_71,55,0,30.9,1,0,0
+Samp_72,57,0,26.8,1,1,1
+Samp_73,63,0,32.3,0,1,0
+Samp_74,65,1,28.4,1,1,1
+Samp_75,64,1,30.2,0,1,0
+Samp_76,80,1,38.7,1,0,0
+Samp_77,68,1,26.8,0,1,1
+Samp_78,54,0,39.2,0,1,0
+Samp_79,40,0,38,1,1,1
+Samp_80,64,0,22.7,1,0,0
+Samp_81,46,1,20,0,0,1
+Samp_82,48,0,20.7,0,1,1
+Samp_83,63,0,18.9,1,0,1
+Samp_84,40,1,20.5,0,1,1
+Samp_85,47,1,33.2,1,0,0
+Samp_86,63,0,20,1,0,0
+Samp_87,50,0,25.4,1,0,1
+Samp_88,56,0,36.7,1,0,1
+Samp_89,47,0,19,0,1,1
+Samp_90,74,0,36,1,1,0
+Samp_91,74,0,24.6,0,1,0
+Samp_92,72,1,21,1,0,0
+Samp_93,44,1,33.5,0,1,0
+Samp_94,78,1,32,1,1,1
+Samp_95,80,0,37.4,0,1,1
+Samp_96,67,1,34.3,0,0,1
+Samp_97,46,0,35.8,1,0,0
+Samp_98,48,0,24.6,1,0,0
+Samp_99,47,1,22.3,1,1,0
+Samp_100,51,0,34.6,1,1,1
+Samp_101,73,0,35.8,0,1,1
+Samp_102,72,1,39.8,1,0,0
+Samp_103,62,1,27.4,0,0,1
+Samp_104,63,1,26.5,0,1,0
+Samp_105,76,0,35.2,1,0,0
+Samp_106,74,1,25.8,0,0,1
+Samp_107,79,1,38.5,0,0,0
+Samp_108,61,0,37,0,0,1
+Samp_109,66,1,27.7,0,1,0
+Samp_110,74,1,34.6,1,0,1
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diff --git a/bioneuralnet/downstream_task/dpmon.py b/bioneuralnet/downstream_task/dpmon.py
index f205492..ca9424f 100644
--- a/bioneuralnet/downstream_task/dpmon.py
+++ b/bioneuralnet/downstream_task/dpmon.py
@@ -1,30 +1,27 @@
 import os
 import re
-import logging
 import statistics
 import tempfile
 from typing import Optional, List
-
+import shutil
 import torch
 import torch.nn as nn
 import torch.optim as optim
-import torch.nn.functional as F
 import pandas as pd
-import numpy as np
 import networkx as nx
 from torch_geometric.data import Data
 from torch_geometric.transforms import RandomNodeSplit
+from ray import train
 from ray import tune
+from ray.tune import Checkpoint
 from ray.tune import CLIReporter
 from ray.tune.schedulers import ASHAScheduler
-from ray import train
-from ray.train import Checkpoint
-
-from ..network_embedding.gnn_models import GCN, GAT, SAGE, GIN
+from bioneuralnet.utils import get_logger
+from pathlib import Path
 
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
+from bioneuralnet.network_embedding.gnn_models import GCN, GAT, SAGE, GIN
 
+logger= get_logger(__name__)
 
 class DPMON:
     def __init__(
@@ -38,9 +35,9 @@ def __init__(
         layer_num: int = 5,
         nn_hidden_dim1: int = 8,
         nn_hidden_dim2: int = 8,
-        epoch_num: int = 5,
+        num_epochs: int = 100,
         repeat_num: int = 5,
-        lr: float = 0.01,
+        lr: float = 1e-1,
         weight_decay: float = 1e-4,
         tune: bool = False,
         gpu: bool = False,
@@ -69,16 +66,23 @@ def __init__(
         self.layer_num = layer_num
         self.nn_hidden_dim1 = nn_hidden_dim1
         self.nn_hidden_dim2 = nn_hidden_dim2
-        self.epoch_num = epoch_num
+        self.num_epochs = num_epochs
         self.repeat_num = repeat_num
         self.lr = lr
         self.weight_decay = weight_decay
         self.tune = tune
         self.gpu = gpu
         self.cuda = cuda
-        self.output_dir = output_dir if output_dir else f"dpmon_output"
 
-        os.makedirs(self.output_dir, exist_ok=True)
+        if output_dir is None:
+            self.output_dir = Path(os.getcwd()) / "dpmon"  
+        else:
+            self.output_dir = Path(output_dir)
+
+        self.output_dir.mkdir(parents=True, exist_ok=True)  
+        logger.info(f"Output directory set to: {self.output_dir}")
+
+
         logger.info("Initialized DPMON with the provided parameters.")
 
     def run(self) -> pd.DataFrame:
@@ -128,7 +132,7 @@ def run(self) -> pd.DataFrame:
             "layer_num": self.layer_num,
             "nn_hidden_dim1": self.nn_hidden_dim1,
             "nn_hidden_dim2": self.nn_hidden_dim2,
-            "epoch_num": self.epoch_num,
+            "num_epochs": self.num_epochs,
             "repeat_num": self.repeat_num,
             "lr": self.lr,
             "weight_decay": self.weight_decay,
@@ -139,6 +143,8 @@ def run(self) -> pd.DataFrame:
 
         # Combine omics datasets
         combined_omics = pd.concat(self.omics_list, axis=1)
+        combined_omics = combined_omics[self.adjacency_matrix.columns]
+        
         if "phenotype" not in combined_omics.columns:
             combined_omics = combined_omics.merge(
                 self.phenotype_data[["phenotype"]],
@@ -148,22 +154,42 @@ def run(self) -> pd.DataFrame:
 
         if self.tune:
             logger.info("Running hyperparameter tuning for DPMON.")
-            run_hyperparameter_tuning(
+            best_config_df = run_hyperparameter_tuning(
                 dpmon_params, self.adjacency_matrix, combined_omics, self.clinical_data
             )
-            return pd.DataFrame()
-
-        logger.info("Running standard training for DPMON.")
-        predictions = run_standard_training(
-            dpmon_params,
-            self.adjacency_matrix,
-            combined_omics,
-            self.clinical_data,
-            output_dir=self.output_dir,
-        )
+            logger.info(best_config_df)
+            best_config = best_config_df.iloc[0].to_dict()
+            best_config["gnn_hidden_dim"] = int(best_config["gnn_hidden_dim"])
+            best_config["gnn_layer_num"] = int(best_config["gnn_layer_num"])
+            best_config["nn_hidden_dim1"] = int(best_config["nn_hidden_dim1"])
+            best_config["nn_hidden_dim2"] = int(best_config["nn_hidden_dim2"])
+            best_config["num_epochs"] = int(best_config["num_epochs"])
+            logger.info(f"Best tuned parameters: {best_config}")
+            
+            logger.info(f"Best tuned parameters: {best_config}")
+            dpmon_params.update(best_config)
+            logger.info("Running standard training with tuned parameters.")
+            predictions = run_standard_training(
+                dpmon_params,
+                self.adjacency_matrix,
+                combined_omics,
+                self.clinical_data,
+                output_dir=self.output_dir,
+            )
+            return predictions
+        
+        else:
+            logger.info("Running standard training for DPMON.")
+            predictions = run_standard_training(
+                dpmon_params,
+                self.adjacency_matrix,
+                combined_omics,
+                self.clinical_data,
+                output_dir=self.output_dir,
+            )
 
-        logger.info("DPMON run completed.")
-        return predictions
+            logger.info("DPMON run completed.")
+            return predictions
 
 
 def setup_device(gpu, cuda):
@@ -260,21 +286,19 @@ def build_omics_networks_tg(
     return [data]
 
 
-def run_standard_training(
-    dpmon_params, adjacency_matrix, combined_omics, clinical_data, output_dir
-):
+def run_standard_training(dpmon_params, adjacency_matrix, combined_omics, clinical_data, output_dir):
     device = setup_device(dpmon_params["gpu"], dpmon_params["cuda"])
     omics_dataset = slice_omics_datasets(combined_omics, adjacency_matrix)
-    omics_networks_tg = build_omics_networks_tg(
-        adjacency_matrix, omics_dataset, clinical_data
-    )
+    omics_networks_tg = build_omics_networks_tg(adjacency_matrix, omics_dataset, clinical_data)
 
     accuracies = []
-    last_predictions_df = pd.DataFrame()
+    best_accuracy = 0
+    best_predictions_df = None
 
     for omics_data, omics_network in zip(omics_dataset, omics_networks_tg):
         for i in range(dpmon_params["repeat_num"]):
             logger.info(f"Training iteration {i+1}/{dpmon_params['repeat_num']}")
+
             model = NeuralNetwork(
                 model_type=dpmon_params["model"],
                 gnn_input_dim=omics_network.x.shape[1],
@@ -294,55 +318,41 @@ def run_standard_training(
                 weight_decay=dpmon_params["weight_decay"],
             )
 
-            train_features = torch.FloatTensor(
-                omics_data.drop(["phenotype"], axis=1).values
-            ).to(device)
+            train_features = torch.FloatTensor(omics_data.drop(["phenotype"], axis=1).values).to(device)
             train_labels = {
-                "labels": torch.LongTensor(omics_data["phenotype"].values.copy()).to(
-                    device
-                ),
+                "labels": torch.LongTensor(omics_data["phenotype"].values.copy()).to(device),
                 "omics_network": omics_network.to(device),
             }
 
-            accuracy = train_model(
-                model,
-                criterion,
-                optimizer,
-                train_features,
-                train_labels,
-                dpmon_params["epoch_num"],
-            )
+            accuracy = train_model(model, criterion, optimizer, train_features, train_labels, dpmon_params["num_epochs"])
             accuracies.append(accuracy)
+
+            # Save model
             model_path = os.path.join(output_dir, f"dpm_model_iter_{i+1}.pth")
             torch.save(model.state_dict(), model_path)
             logger.info(f"Model saved to {model_path}")
 
+            # Evaluate model
             model.eval()
             with torch.no_grad():
                 predictions, _ = model(train_features, omics_network.to(device))
                 _, predicted = torch.max(predictions, 1)
-                predictions_path = os.path.join(
-                    output_dir, f"predictions_iter_{i+1}.csv"
-                )
                 predictions_df = pd.DataFrame(
-                    {
-                        "Actual": omics_data["phenotype"].values,
-                        "Predicted": predicted.cpu().numpy(),
-                    }
+                    {"Actual": omics_data["phenotype"].values, "Predicted": predicted.cpu().numpy()}
                 )
-                predictions_df.to_csv(predictions_path, index=False)
-                logger.info(f"Predictions saved to {predictions_path}")
-                last_predictions_df = predictions_df
+
+            if accuracy > best_accuracy:
+                best_accuracy = accuracy
+                best_predictions_df = predictions_df
 
     if accuracies:
-        max_accuracy = max(accuracies)
         avg_accuracy = sum(accuracies) / len(accuracies)
         std_accuracy = statistics.stdev(accuracies) if len(accuracies) > 1 else 0.0
-        logger.info(f"Best Accuracy: {max_accuracy:.4f}")
+        logger.info(f"Best Accuracy: {best_accuracy:.4f}")
         logger.info(f"Average Accuracy: {avg_accuracy:.4f}")
         logger.info(f"Standard Deviation of Accuracy: {std_accuracy:.4f}")
 
-    return last_predictions_df
+    return best_predictions_df, avg_accuracy
 
 
 def run_hyperparameter_tuning(
@@ -367,12 +377,13 @@ def run_hyperparameter_tuning(
 
     reporter = CLIReporter(metric_columns=["loss", "accuracy", "training_iteration"])
     scheduler = ASHAScheduler(
-        metric="loss", mode="min", grace_period=10, reduction_factor=2
+        metric="loss", mode="min", grace_period=1, reduction_factor=2
     )
     gpu_resources = 1 if dpmon_params["gpu"] else 0
 
+    best_configs = []
+    
     for omics_data, omics_network_tg in zip(omics_dataset, omics_networks_tg):
-        os.environ["TUNE_DISABLE_STRICT_METRIC_CHECKING"] = "1"
         logger.info(
             f"Starting hyperparameter tuning for dataset shape: {omics_data.shape}"
         )
@@ -449,15 +460,19 @@ def tune_train_n(config):
                         metrics=metrics, checkpoint=Checkpoint.from_directory(tempdir)
                     )
 
+        def short_dirname_creator(trial):
+            return f"T{trial.trial_id}"
+
         result = tune.run(
             tune_train_n,
             resources_per_trial={"cpu": 2, "gpu": gpu_resources},
             config=pipeline_configs,
             num_samples=10,
+            verbose=0,
             scheduler=scheduler,
-            name="Hyperparameter_Tuning",
+            name="tune_dp",
             progress_reporter=reporter,
-            keep_checkpoints_num=1,
+            trial_dirname_creator=short_dirname_creator,
             checkpoint_score_attr="min-loss",
         )
 
@@ -467,6 +482,10 @@ def tune_train_n(config):
         logger.info(
             "Best trial final accuracy: {}".format(best_trial.last_result["accuracy"])
         )
+        best_configs.append(best_trial.config)
+
+    best_configs_df = pd.DataFrame(best_configs)
+    return best_configs_df
 
 
 def train_model(model, criterion, optimizer, train_data, train_labels, epoch_num):
diff --git a/bioneuralnet/external_tools/SmCCNet.R b/bioneuralnet/external_tools/SmCCNet.R
index 7f5493c..5136525 100644
--- a/bioneuralnet/external_tools/SmCCNet.R
+++ b/bioneuralnet/external_tools/SmCCNet.R
@@ -3,12 +3,11 @@
 library("SmCCNet")
 library("WGCNA")
 library("jsonlite")
-library("dplyr")
+library("dplyr") 
 
 options(stringsAsFactors = FALSE)
 allowWGCNAThreads()
 
-# Read JSON input from stdin
 json_input <- readLines(con = "stdin")
 if (length(json_input) == 0) {
   stop("No input data received.")
@@ -30,121 +29,181 @@ if (length(omics_keys) < 1) {
   stop("No omics data found in input.")
 }
 
-# Initialize list to store each omics matrix
 omics_list <- list()
-
-# Loop over all omics keys
 for (key in omics_keys) {
   omics_df <- read.csv(text = input_data[[key]], stringsAsFactors = FALSE)
   if (!("SampleID" %in% colnames(omics_df))) {
     stop(paste("SampleID column not found in", key))
   }
   rownames(omics_df) <- omics_df$SampleID
-  # Convert to matrix: assuming the first column are the feature names
+  
   omics_values <- as.matrix(omics_df[, -1])
-  # Standardize features
-  omics_values <- scale(omics_values)
-  # Subset to common samples with phenotype
+
   common_samples <- intersect(rownames(omics_values), rownames(phenotype_df))
   if (length(common_samples) == 0) {
     stop(paste("No matching sample IDs between", key, "and phenotype data."))
   }
-  omics_values <- omics_values[common_samples, , drop = FALSE]
-  # Store in list
-  omics_list[[length(omics_list) + 1]] <- omics_values
+  
+  omics_values <- omics_values[common_samples, , drop=FALSE]
+  omics_list[[length(omics_list)+1]] <- omics_values
 }
 
-# Also subset phenotype data to common samples across all omics.
-# in here we are assuming that all omics datasets share the same samples.
 common_samples_all <- rownames(phenotype_df)
 for (mat in omics_list) {
   common_samples_all <- intersect(common_samples_all, rownames(mat))
 }
 if (length(common_samples_all) == 0) {
-  stop("No common samples across all omics datasets and phenotype data.")
+  stop("No common samples across all omics datasets and phenotype.")
 }
-phenotype_df <- phenotype_df[common_samples_all, , drop = FALSE]
-# Also, subset each omics matrix in the list
-omics_list <- lapply(omics_list, function(mat) mat[common_samples_all, , drop = FALSE])
+phenotype_df <- phenotype_df[common_samples_all, , drop=FALSE]
+omics_list   <- lapply(omics_list, function(m) m[common_samples_all, , drop=FALSE])
+
 
-# Parse command-line arguments
 args <- commandArgs(trailingOnly = TRUE)
-if (length(args) != 4) {
-  stop("Exactly 4 arguments must be supplied: data_types, kfold, summarization, seed")
+if (length(args) < 8) {
+  stop("Expected 8 arguments: data_types, kfold, summarization, seed, eval_method, ncomp_pls, subSampNum, between_shrinkage")
 }
-data_types <- strsplit(args[1], ",")[[1]]
-kfold <- as.numeric(args[2])
+
+data_types    <- strsplit(args[1], ",")[[1]]
+kfold         <- as.numeric(args[2])
 summarization <- args[3]
-seed <- as.numeric(args[4])
+seed          <- as.numeric(args[4])
+eval_method   <- args[5]
+ncomp_pls_arg <- args[6]
+subSampNum    <- as.numeric(args[7])
+bShrink       <- as.numeric(args[8])
 
-if (is.na(kfold) || is.na(seed)) {
-  stop("kfold and seed must be numeric values.")
-}
-if (!(summarization %in% c("PCA", "SVD", "NetSHy"))) {
-  stop("Summarization method must be 'PCA', 'SVD', or 'NetSHy'.")
+if (ncomp_pls_arg == "") {
+  ncomp_pls <- NULL
+} else {
+  ncomp_pls <- as.numeric(ncomp_pls_arg)
 }
+
 set.seed(seed)
 
-# Ensure that the number of data_types matches the number of omics datasets
 if (length(data_types) != length(omics_list)) {
-  stop("Number of omics datasets does not match number of data types provided.")
+  stop("data_types length doesn't match number of omics datasets.")
 }
 
-# Extract phenotype vector (assume phenotype is in second column) since R is 1-indexed
 Y <- as.numeric(phenotype_df[[2]])
 if (any(is.na(Y))) {
-  stop("Phenotype vector contains NA values.")
+  stop("Phenotype contains NA.")
 }
 
-tryCatch(
-  {
-    if (length(data_types) == 1) {
-      message("Single-omics mode detected.")
-      if (summarization != "NetSHy") {
-        message("Overriding summarization method to 'NetSHy' for single-omics analysis.")
-        summarization <- "NetSHy"
-      }
-      if (length(unique(Y)) == 2) {
-        message("Binary phenotype detected. Setting ncomp_pls = 5 for single-omics PLS.")
-        ncomp_pls <- 5
-      } else {
-        ncomp_pls <- NULL
-      }
-    } else {
-      message("Multi-omics mode detected.")
-      ncomp_pls <- NULL
+message("DEBUG: We have ", length(Y), " samples in Y. Range: [", min(Y), ", ", max(Y), "]")
+message("DEBUG: Omics data shapes:")
+for (i in seq_along(omics_list)) {
+  message("  Omics ", i, ": ", nrow(omics_list[[i]]), " x ", ncol(omics_list[[i]]))
+}
+
+foldIndices <- split.default(seq_len(nrow(omics_list[[1]])), sample(seq_len(nrow(omics_list[[1]])), kfold))
+for (f in seq_along(foldIndices)) {
+  idx <- foldIndices[[f]]
+  message("Debug fold #", f, " => size: ", length(idx))
+  for (om in seq_along(omics_list)) {
+    subset_omics <- omics_list[[om]][idx, , drop=FALSE]
+    var_ <- apply(subset_omics, 2, var, na.rm=TRUE)
+
+    bad_cols <- which(is.na(var_) | is.nan(var_) | var_ == 0)
+    if (length(bad_cols) > 0) {
+      subset_omics <- subset_omics[, -bad_cols, drop=FALSE]
+      message("Fold ", f, ", omics #", om, " => removing ", length(bad_cols), 
+              " zero/NA-variance cols.")
     }
+  }
+}
+
+Y_binary <- ifelse(Y > median(Y), 1, 0) 
+
+if (length(data_types) == 1 && !is.null(ncomp_pls)) {
+  message("Single-omics PLS scenario")
+  result <- fastAutoSmCCNet(
+    X = omics_list,
+    Y = as.factor(Y_binary),
+    DataType = data_types,
+    Kfold = kfold,
+    subSampNum = subSampNum,
+    CutHeight = 1 - 0.1^10,
+    EvalMethod = "auc",
+    summarization = summarization,
+    ncomp_pls = ncomp_pls,
+    seed = seed
+  )
+  
+} else if (length(data_types) == 1) {
+  message("Single-omics CCA scenario")
+
+  result <- fastAutoSmCCNet(
+    X = omics_list,
+    Y = Y,
+    DataType = data_types,
+    Kfold = kfold,
+    subSampNum = subSampNum,
+    summarization = summarization,
+    CutHeight = 1 - 0.1^10,
+    seed = seed
+  )
+  
+} else if (length(data_types) > 1 && !is.null(ncomp_pls)) {
+  message("Multi-omics PLS scenario")
+  result <- fastAutoSmCCNet(
+    X = omics_list,
+    Y = as.factor(Y),
+    DataType = data_types,
+    EvalMethod = "auc",
+    Kfold = kfold,
+    subSampNum = subSampNum,
+    summarization = summarization,
+    CutHeight = 1 - 0.1^10,
+    ncomp_pls = ncomp_pls,
+    seed = seed,
+    BetweenShrinkage = bShrink
+  )
+  
+} else {
+  message("Multi-omics CCA scenario")
+  result <- fastAutoSmCCNet(
+    X = omics_list,
+    Y = Y,
+    DataType = data_types,
+    Kfold = kfold,
+    subSampNum = subSampNum,
+    summarization = summarization,
+    CutHeight = 1 - 0.1^10,
+    seed = seed,
+    BetweenShrinkage = bShrink
+  )
+}
+
+write.csv(result$AdjacencyMatrix, file = "GlobalNetwork.csv",  row.names = TRUE)
+
+current_dir <- getwd()
+message("Current working directory: ", current_dir)
+pattern <- "^size_.*\\.Rdata$"
+rdata_files <- list.files(path = current_dir, pattern = pattern, full.names = TRUE)
+message("Found files: ", paste(rdata_files, collapse = ", "))
 
-    if (!is.null(ncomp_pls)) {
-      result <- fastAutoSmCCNet(
-        X = omics_list,
-        Y = Y,
-        DataType = data_types,
-        Kfold = kfold,
-        subSampNum = 50,
-        EvalMethod = "accuracy",
-        summarization = summarization,
-        ncomp_pls = ncomp_pls,
-        seed = seed
-      )
+if (length(rdata_files) == 0) {
+  message("No RData files found in the current directory.\n")
+} else {
+  for (file in rdata_files) {
+    message("Processing file: ", file, "\n")
+    
+    temp_env <- new.env()
+    loaded_names <- load(file, envir = temp_env)
+    
+    if ("M" %in% loaded_names && exists("M", envir = temp_env)) {
+      sub_net <- get("M", envir = temp_env)
+      
+      file_base   <- tools::file_path_sans_ext(basename(file))
+      csv_filename <- paste0(file_base, ".csv")
+      
+      write.csv(sub_net, file = csv_filename, row.names = TRUE)
+      message("Subnetwork matrix from ", file, " written to ", csv_filename, "\n\n")
     } else {
-      result <- fastAutoSmCCNet(
-        X = omics_list,
-        Y = Y,
-        DataType = data_types,
-        Kfold = kfold,
-        subSampNum = 50,
-        EvalMethod = "accuracy",
-        summarization = summarization,
-        seed = seed
-      )
+      message("Warning: Object 'M' was not found in ", file, "\n\n")
     }
-  },
-  error = function(e) {
-    message("Error during fastAutoSmCCNet execution: ", conditionMessage(e))
-    quit(status = 1)
   }
-)
-# Write output to a csv
-write.csv(result$AdjacencyMatrix, file = "AdjacencyMatrix.csv", row.names = TRUE)
-quit(status = 0)
+}
+
+quit(status = 0)
\ No newline at end of file
diff --git a/bioneuralnet/external_tools/__init__.py b/bioneuralnet/external_tools/__init__.py
index f9ce6e9..9d58877 100644
--- a/bioneuralnet/external_tools/__init__.py
+++ b/bioneuralnet/external_tools/__init__.py
@@ -1,17 +1,9 @@
 from .smccnet import SmCCNet
 from .wgcna import WGCNA
 from .node2vec import node2vec as Node2Vec
-from .feature_selector import FeatureSelector
-from .static_visualization import StaticVisualizer
-from .dynamic_visualization import DynamicVisualizer
-from .hierarchical import HierarchicalClustering
 
 __all__ = [
     "SmCCNet",
     "WGCNA",
     "Node2Vec",
-    "FeatureSelector",
-    "StaticVisualizer",
-    "DynamicVisualizer",
-    "HierarchicalClustering",
 ]
diff --git a/bioneuralnet/external_tools/dynamic_visualization.py b/bioneuralnet/external_tools/dynamic_visualization.py
deleted file mode 100644
index 8c8c831..0000000
--- a/bioneuralnet/external_tools/dynamic_visualization.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-from typing import Optional
-import pandas as pd
-import networkx as nx
-import pyvis
-from pyvis.network import Network
-from jinja2 import Environment, FileSystemLoader
-from ..utils.logger import get_logger
-
-
-class DynamicVisualizer:
-    def __init__(
-        self,
-        adjacency_matrix: pd.DataFrame,
-        layout: str = "spring",
-        notebook: bool = False,
-        bgcolor: str = "#ffffff",
-        font_color: str = "black",
-        output_dir: Optional[str] = None,
-        output_filename: str = "dynamic_network.html",
-        width: str = "100%",
-        height: str = "800px",
-    ):
-        self.adjacency_matrix = adjacency_matrix
-        self.layout = layout
-        self.notebook = notebook
-        self.bgcolor = bgcolor
-        self.font_color = font_color
-        self.output_dir = output_dir if output_dir else os.getcwd()
-        self.output_filename = output_filename
-        self.width = width
-        self.height = height
-        self.logger = get_logger(__name__)
-        self.logger.info("Initialized DynamicVisualizer.")
-
-    def generate_graph(self) -> nx.Graph:
-        self.logger.info("Generating NetworkX graph from adjacency_matrix.")
-        G = nx.from_pandas_adjacency(self.adjacency_matrix)
-        self.logger.info(
-            f"Graph generated with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges."
-        )
-        if G.number_of_nodes() == 0:
-            self.logger.error("Generated graph is empty.")
-            raise nx.NetworkXError("Generated graph is empty.")
-        return G
-
-    def visualize(self, G: nx.Graph):
-        self.logger.info(
-            f"Generating interactive visualization with layout: {self.layout}"
-        )
-
-        templates_dir = os.path.join(os.path.dirname(pyvis.__file__), "templates")
-        env = Environment(loader=FileSystemLoader(templates_dir))
-        template = env.get_template("template.html")
-
-        net = Network(
-            height=self.height,
-            width=self.width,
-            bgcolor=self.bgcolor,
-            font_color=self.font_color,
-            notebook=False,
-        )
-        net.template = template
-
-        if self.layout == "hierarchical":
-            net.barnes_hut()
-        elif self.layout == "spring":
-            net.force_atlas_2based()
-        else:
-            self.logger.warning(
-                f"Layout '{self.layout}' not recognized. Falling back to spring layout."
-            )
-
-        net.from_nx(G)
-
-        for node in net.nodes:
-            node["title"] = node["id"]
-            node["label"] = node["id"]
-            node["color"] = "skyblue"
-
-        for edge in net.edges:
-            edge["color"] = "gray"
-
-        os.makedirs(self.output_dir, exist_ok=True)
-        output_path = os.path.join(self.output_dir, self.output_filename)
-        net.show(output_path)
-        self.logger.info(f"Interactive network visualization saved to {output_path}")
diff --git a/bioneuralnet/external_tools/feature_selector.py b/bioneuralnet/external_tools/feature_selector.py
deleted file mode 100644
index 4f8db5d..0000000
--- a/bioneuralnet/external_tools/feature_selector.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import pandas as pd
-from sklearn.linear_model import LassoCV
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.feature_selection import SelectKBest, f_classif
-from ..utils.logger import get_logger
-
-
-class FeatureSelector:
-    """
-    FeatureSelector Class for Selecting Relevant Multi-Omics Features.
-
-    This class provides methods for feature selection using statistical and machine learning-based approaches.
-    It allows selection based on correlation, LASSO regression, or Random Forest feature importances.
-    """
-
-    def __init__(
-        self,
-        enhanced_omics_data: pd.DataFrame,
-        phenotype_data: pd.Series,
-        num_features: int = 10,
-        selection_method: str = "correlation",
-    ):
-        """
-        Initializes the FeatureSelector instance.
-
-        Args:
-            enhanced_omics_data (pd.DataFrame): Enhanced multi-omics dataset with integrated embeddings.
-            phenotype_data (pd.Series): Phenotype data corresponding to the samples.
-            num_features (int, optional): Number of top features to select. Defaults to 10.
-            selection_method (str, optional): Feature selection method ('correlation', 'lasso', 'random_forest'). Defaults to 'correlation'.
-            #output_dir (str, optional): Directory to save selected features. If None, creates a unique directory.
-        """
-        self.logger = get_logger(__name__)
-        self.logger.info("Initialized FeatureSelector.")
-        self.num_features = num_features
-        self.selection_method = selection_method
-        self.enhanced_omics_data = enhanced_omics_data
-        self.phenotype_data = phenotype_data
-
-    def perform_feature_selection(self) -> pd.DataFrame:
-        """
-        Performs feature selection on the enhanced omics data based on the selected method.
-
-        Returns:
-            pd.DataFrame: DataFrame containing the selected features.
-
-        Raises:
-            ValueError: If an unsupported feature selection method is specified.
-        """
-        self.logger.info(
-            f"Performing feature selection using method: {self.selection_method}"
-        )
-
-        if self.selection_method == "correlation":
-            selected_features = self._correlation_based_selection()
-        elif self.selection_method == "lasso":
-            selected_features = self._lasso_based_selection()
-        elif self.selection_method == "random_forest":
-            selected_features = self._random_forest_based_selection()
-        else:
-            self.logger.error(
-                f"Unsupported feature selection method: {self.selection_method}"
-            )
-            raise ValueError(
-                f"Unsupported feature selection method: {self.selection_method}"
-            )
-
-        return selected_features
-
-    def _correlation_based_selection(self) -> pd.DataFrame:
-        """
-        Selects top features based on correlation with phenotype using ANOVA.
-
-        Returns:
-            pd.DataFrame: DataFrame containing the selected features.
-        """
-        self.logger.info(
-            "Performing correlation-based feature selection using ANOVA (f_classif)."
-        )
-        selector = SelectKBest(score_func=f_classif, k=self.num_features)
-        selector.fit(self.enhanced_omics_data, self.phenotype_data)
-        selected_mask = selector.get_support()
-        selected_features = self.enhanced_omics_data.columns[selected_mask]
-        self.logger.info(
-            f"Selected {len(selected_features)} features based on correlation."
-        )
-        return self.enhanced_omics_data[selected_features]
-
-    def _lasso_based_selection(self) -> pd.DataFrame:
-        """
-        Selects top features based on LASSO regression coefficients.
-
-        Returns:
-            pd.DataFrame: DataFrame containing the selected features.
-        """
-        self.logger.info("Performing LASSO-based feature selection.")
-        # Ensure cv does not exceed number of samples
-        n_samples = len(self.enhanced_omics_data)
-        cv_folds = 5
-        if n_samples < cv_folds:
-            cv_folds = n_samples
-            self.logger.warning(
-                f"Reducing cv from 5 to {cv_folds} due to insufficient samples."
-            )
-        if cv_folds < 2:
-            raise ValueError(
-                f"Number of splits {cv_folds} must be at least 2 for cross-validation."
-            )
-
-        lasso = LassoCV(cv=cv_folds, random_state=0).fit(
-            self.enhanced_omics_data, self.phenotype_data
-        )
-        coef = pd.Series(lasso.coef_, index=self.enhanced_omics_data.columns)
-        selected_features = (
-            coef.abs().sort_values(ascending=False).head(self.num_features).index
-        )
-        self.logger.info(
-            f"Selected {len(selected_features)} features based on LASSO coefficients."
-        )
-        return self.enhanced_omics_data[selected_features]
-
-    def _random_forest_based_selection(self) -> pd.DataFrame:
-        """
-        Selects top features based on Random Forest feature importances.
-
-        Returns:
-            pd.DataFrame: DataFrame containing the selected features.
-        """
-        self.logger.info("Performing Random Forest-based feature selection.")
-        rf = RandomForestClassifier(n_estimators=100, random_state=0)
-        rf.fit(self.enhanced_omics_data, self.phenotype_data)
-        importances = pd.Series(
-            rf.feature_importances_, index=self.enhanced_omics_data.columns
-        )
-        selected_features = (
-            importances.sort_values(ascending=False).head(self.num_features).index
-        )
-        self.logger.info(
-            f"Selected {len(selected_features)} features based on Random Forest importances."
-        )
-        return self.enhanced_omics_data[selected_features]
-
-    def run_feature_selection(self) -> pd.DataFrame:
-        """
-        Executes the feature selection process and saves the results.
-
-        Returns:
-            pd.DataFrame: DataFrame containing the selected features.
-        """
-        self.logger.info("Starting feature selection on enhanced omics data.")
-        selected_features = self.perform_feature_selection()
-        self.logger.info(
-            "Feature selection on enhanced omics data completed successfully."
-        )
-        return selected_features
diff --git a/bioneuralnet/external_tools/hierarchical.py b/bioneuralnet/external_tools/hierarchical.py
deleted file mode 100644
index 844fd84..0000000
--- a/bioneuralnet/external_tools/hierarchical.py
+++ /dev/null
@@ -1,197 +0,0 @@
-from typing import Dict, Any
-import pandas as pd
-from sklearn.cluster import AgglomerativeClustering
-from sklearn.metrics import silhouette_score
-from sklearn.preprocessing import StandardScaler
-from ..utils.logger import get_logger
-
-
-class HierarchicalClustering:
-    """
-    HierarchicalClustering Class for Performing Agglomerative Hierarchical Clustering.
-
-    This class handles the execution of hierarchical clustering on a provided adjacency matrix DataFrame
-    and returns clustering results without relying on file I/O operations.
-
-    Attributes:
-        n_clusters (int): Number of clusters to find.
-        linkage (str): Linkage criterion to use ('ward', 'complete', 'average', 'single').
-        affinity (str): Metric used to compute linkage ('euclidean', 'l1', 'l2', 'manhattan', 'cosine', etc.).
-        scaler (Optional[StandardScaler]): Scaler for data normalization.
-    """
-
-    def __init__(
-        self,
-        adjacency_matrix: pd.DataFrame,
-        n_clusters: int = 2,
-        linkage: str = "ward",
-        affinity: str = "euclidean",
-        scale_data: bool = True,
-    ):
-        """
-        Initializes the HierarchicalClustering instance.
-
-        Args:
-            adjacency_matrix (pd.DataFrame): Adjacency matrix representing feature relationships.
-            n_clusters (int, optional): Number of clusters to find. Defaults to 2.
-            linkage (str, optional): Linkage criterion to use. Defaults to 'ward'.
-            affinity (str, optional): Metric used to compute linkage. Defaults to 'euclidean'.
-            scale_data (bool, optional): Whether to standardize the data. Defaults to True.
-        """
-
-        if linkage == "ward" and affinity != "euclidean":
-            raise ValueError(
-                "The 'ward' linkage method only supports the 'euclidean' metric."
-            )
-        if linkage not in ["ward", "complete", "average", "single"]:
-            raise ValueError(f"Unsupported linkage method: {linkage}")
-        if affinity not in ["euclidean", "l1", "l2", "manhattan", "cosine"]:
-            raise ValueError(f"Unsupported affinity metric: {affinity}")
-
-        self.n_clusters = n_clusters
-        self.linkage = linkage
-        self.affinity = affinity
-        self.scale_data = scale_data
-
-        # Initialize logger
-        self.logger = get_logger(__name__)
-        self.logger.info(
-            "Initialized HierarchicalClustering with the following parameters:"
-        )
-        self.logger.info(f"Number of Clusters: {self.n_clusters}")
-        self.logger.info(f"Linkage: {self.linkage}")
-        self.logger.info(f"Affinity: {self.affinity}")
-        self.logger.info(f"Scale Data: {self.scale_data}")
-
-        self.adjacency_matrix = adjacency_matrix
-        self.scaler = StandardScaler() if self.scale_data else None
-        self.scaled_feature_matrix = None
-        self.labels = None
-        self.silhouette_avg = None
-        self.cluster_labels_df = None
-
-    def preprocess_data(self) -> None:
-        """
-        Preprocesses the adjacency matrix by scaling the data if required.
-        """
-        try:
-            if self.scale_data and self.scaler is not None:
-                scaled_array = self.scaler.fit_transform(self.adjacency_matrix.values)
-                self.scaled_feature_matrix = pd.DataFrame(
-                    scaled_array,
-                    index=self.adjacency_matrix.index,
-                    columns=self.adjacency_matrix.columns,
-                )
-                self.logger.info("Data has been scaled using StandardScaler.")
-            else:
-                self.scaled_feature_matrix = self.adjacency_matrix.copy()
-                self.logger.info("Data scaling skipped.")
-        except Exception as e:
-            self.logger.error(f"Error during data preprocessing: {e}")
-            raise
-
-    def run_clustering(self) -> None:
-        """
-        Executes the hierarchical clustering algorithm.
-        """
-        try:
-            metric = "euclidean" if self.linkage == "ward" else self.affinity
-            model = AgglomerativeClustering(
-                n_clusters=self.n_clusters, linkage=self.linkage, metric=metric
-            )
-
-            self.labels = model.fit_predict(self.scaled_feature_matrix)
-            self.logger.info("Hierarchical clustering completed.")
-
-            if metric in ["euclidean", "l1", "l2", "manhattan", "cosine"]:
-                try:
-                    self.silhouette_avg = silhouette_score(
-                        self.scaled_feature_matrix, self.labels, metric=metric
-                    )
-                    self.logger.info(f"Silhouette Score: {self.silhouette_avg:.4f}")
-                except Exception as e:
-                    self.logger.warning(f"Could not compute silhouette score: {e}")
-                    self.silhouette_avg = None
-            else:
-                self.logger.warning(
-                    f"Silhouette score not computed for metric '{metric}'."
-                )
-                self.silhouette_avg = None
-
-            self.cluster_labels_df = pd.DataFrame(
-                {"node": self.adjacency_matrix.index, "cluster": self.labels}
-            )
-            self.logger.info("Cluster labels DataFrame created.")
-
-        except Exception as e:
-            self.logger.error(f"Error during clustering: {e}")
-            raise
-
-    def get_results(self) -> Dict[str, Any]:
-        """
-        Retrieves the clustering results.
-
-        Returns:
-            Dict[str, Any]: Dictionary containing cluster labels and silhouette score.
-        """
-        if self.cluster_labels_df is None:
-            self.logger.error("Clustering has not been run yet.")
-            raise ValueError("Clustering has not been run yet.")
-
-        results = {
-            "cluster_labels": self.cluster_labels_df,
-            "silhouette_score": self.silhouette_avg,
-        }
-        return results
-
-    def run(self) -> Dict[str, Any]:
-        """
-        Runs the entire hierarchical clustering pipeline.
-
-        **Steps:**
-
-        1. **Preprocessing Data**:
-            - Prepares and formats the input data to be compatible with hierarchical clustering.
-
-        2. **Running Clustering**:
-            - Executes the hierarchical clustering algorithm (e.g., agglomerative clustering) to identify groups or modules in the data.
-
-        3. **Extracting Results**:
-            - Processes and formats clustering outputs, such as cluster assignments and dendrogram structures.
-
-        **Returns**: Dict[str, Any]
-
-            - A dictionary containing clustering results, including:
-              - Cluster assignments for each data point.
-              - Dendrogram structure for visualization.
-              - Metrics or statistics related to cluster quality.
-
-        **Raises**:
-
-            - **ValueError**: If input data is invalid or preprocessing fails.
-            - **Exception**: For any unforeseen issues during the clustering pipeline.
-
-        **Notes**:
-
-            - Ensure that the input data is properly normalized and formatted before running the clustering pipeline.
-            - Adjust clustering parameters like the linkage method or distance metric to fit the analysis.
-
-        **Example**:
-
-        .. code-block:: python
-
-            cluster_pipeline = HierarchicalClustering(data)
-            results = cluster_pipeline.run()
-            print(results['clusters'])
-        """
-        self.logger.info("Running Hierarchical Clustering workflow.")
-
-        try:
-            self.preprocess_data()
-            self.run_clustering()
-            self.logger.info("Hierarchical clustering pipeline completed successfully.")
-            return self.get_results()
-
-        except Exception as e:
-            self.logger.error(f"Error in Hierarchical Clustering: {e}")
-            raise
diff --git a/bioneuralnet/external_tools/node2vec.py b/bioneuralnet/external_tools/node2vec.py
index 037deb8..b44d1ac 100644
--- a/bioneuralnet/external_tools/node2vec.py
+++ b/bioneuralnet/external_tools/node2vec.py
@@ -4,7 +4,6 @@
 from node2vec import Node2Vec
 from ..utils.logger import get_logger
 
-
 class node2vec:
     """
     Node2VecEmbedding Class for Generating Node2Vec-Based Embeddings.
@@ -93,7 +92,7 @@ def _convert_to_networkx(self) -> nx.Graph:
                     if self.weight_key in data:
                         data["weight"] = data.pop(self.weight_key)
                     else:
-                        data["weight"] = 1.0  # Assign default weight if not present
+                        data["weight"] = 1.0 
 
             self.logger.info(
                 f"Converted adjacency matrix to NetworkX graph with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges."
diff --git a/bioneuralnet/external_tools/smccnet.py b/bioneuralnet/external_tools/smccnet.py
index 83084aa..fbb95eb 100644
--- a/bioneuralnet/external_tools/smccnet.py
+++ b/bioneuralnet/external_tools/smccnet.py
@@ -23,48 +23,68 @@ def __init__(
         omics_dfs: List[pd.DataFrame],
         data_types: List[str],
         kfold: int = 5,
+        eval_method: str = "",        
+        subSampNum: int = 1000,
         summarization: str = "NetSHy",
-        seed: int = 732,
-        output_dir: str = None,
+        seed: int = 723,
+        ncomp_pls: int = 0,              
+        between_shrinkage: float = 5.0, 
+        output_dir: str = None
     ):
         """
         Initializes the SmCCNet instance.
 
         Args:
-            phenotype_df (pd.DataFrame): DataFrame containing phenotype data.
+            phenotype_df (pd.DataFrame): DataFrame containing phenotype data, shape [samples x 1 or more].
             omics_dfs (List[pd.DataFrame]): List of omics DataFrames.
-            data_types (List[str]): List of omics data types (e.g., ["protein", "metabolite"]).
-            kfold (int, optional): Number of folds for cross-validation. Defaults to 5.
-            summarization (str, optional): Summarization method. Defaults to "NetSHy".
-            seed (int, optional): Random seed. Defaults to 732.
-            output_dir (str, optional): Folder to write temporary files. If None, a temporary directory is created.
+            data_types (List[str]): List of omics data type strings (e.g. ["Genes", "miRNA"]).
+            kfold (int): Number of folds for cross-validation. Default=5.
+            eval_method (str): e.g. 'accuracy', 'auc', 'f1', or 'Rsquared' (if you patch SmCCNet).
+            subSampNum (int): # of subsamplings. Default=50.
+            summarization (str): 'NetSHy', 'PCA', or 'SVD'. Default='NetSHy'.
+            seed (int): Random seed. Default=123.
+            ncomp_pls (int): # of components for PLS. 0 => no PLS. Default=0.
+            between_shrinkage (float): Shrink factor for multi-omics correlation. Default=5.0.
+            output_dir (str): Folder to write temp files. If None, uses a temporary directory.
         """
         self.phenotype_df = phenotype_df
         self.omics_dfs = omics_dfs
         self.data_types = data_types
         self.kfold = kfold
+        self.eval_method = eval_method
+        self.subSampNum = subSampNum
         self.summarization = summarization
         self.seed = seed
+        self.ncomp_pls = ncomp_pls
+        self.between_shrinkage = between_shrinkage
 
         self.logger = get_logger(__name__)
         self.logger.info("Initialized SmCCNet with parameters:")
         self.logger.info(f"K-Fold: {self.kfold}")
         self.logger.info(f"Summarization: {self.summarization}")
+        self.logger.info(f"Evaluation method: {self.eval_method}")
+        self.logger.info(f"ncomp_pls: {self.ncomp_pls}")
+        self.logger.info(f"subSampNum: {self.subSampNum}")
+        self.logger.info(f"BetweenShrinkage: {self.between_shrinkage}")
         self.logger.info(f"Seed: {self.seed}")
 
         if len(self.omics_dfs) != len(self.data_types):
-            self.logger.error(
-                "Number of omics DataFrames does not match number of data types."
-            )
+            self.logger.error("Number of omics DataFrames does not match number of data types.")
             raise ValueError("Mismatch between omics dataframes and data types.")
-
+        
+        if eval_method in ("auc","accuracy","f1"):
+            uniques = set(phenotype_df.iloc[:, 0].unique())
+            if not uniques.issubset({0,1}):
+                raise ValueError("eval_method=classification, but phenotype is not strictly 0/1.")
+        
+        if eval_method == "Rsquared" and ncomp_pls>0:
+            raise ValueError("Continuous eval can't use PLS. Set ncomp_pls=0 for CCA.")
+
+        # output directory
         if output_dir is None:
-            # Use TemporaryDirectory context manager for automatic cleanup
             self.temp_dir_obj = tempfile.TemporaryDirectory()
             self.output_dir = self.temp_dir_obj.name
-            self.logger.info(
-                f"No output_dir provided; using temporary directory: {self.output_dir}"
-            )
+            self.logger.info(f"No output_dir provided; using temporary directory: {self.output_dir}")
         else:
             self.output_dir = output_dir
             os.makedirs(self.output_dir, exist_ok=True)
@@ -88,17 +108,17 @@ def preprocess_data(self) -> Dict[str, Any]:
             key = f"omics_{i}"
             df = omics_df.copy().reset_index().rename(columns={"index": "SampleID"})
             df["SampleID"] = df["SampleID"].astype(str).str.strip().str.upper()
-            # Intersect sample IDs between phenotype and omics data
+
             common_ids = set(pheno_df["SampleID"]).intersection(set(df["SampleID"]))
             if not common_ids:
-                raise ValueError(
-                    f"No overlapping sample IDs between phenotype and {key}."
-                )
+                raise ValueError(f"No overlapping sample IDs between phenotype and {key}.")
+
             df = df[df["SampleID"].isin(common_ids)]
-            # Ensure ordering follows phenotype data
             df = df.set_index("SampleID").loc[pheno_df["SampleID"]].reset_index()
+
             serialized_data[key] = df.to_csv(index=False)
             self.logger.info(f"Serialized {key} with {len(df)} samples.")
+
         return serialized_data
 
     def run_smccnet(self, serialized_data: Dict[str, Any]) -> None:
@@ -118,10 +138,11 @@ def run_smccnet(self, serialized_data: Dict[str, Any]) -> None:
                 self.logger.error(f"R script not found: {r_script}")
                 raise FileNotFoundError(f"R script not found: {r_script}")
 
-            # Dynamically locate Rscript in the system path
             rscript_path = shutil.which("Rscript")
             if rscript_path is None:
                 raise EnvironmentError("Rscript not found in system PATH.")
+            
+            ncomp_pls_arg = str(self.ncomp_pls) if self.ncomp_pls != 0 else ""
 
             command = [
                 rscript_path,
@@ -130,10 +151,13 @@ def run_smccnet(self, serialized_data: Dict[str, Any]) -> None:
                 str(self.kfold),
                 self.summarization,
                 str(self.seed),
+                self.eval_method,
+                ncomp_pls_arg,
+                str(self.subSampNum),
+                str(self.between_shrinkage),
             ]
-            self.logger.debug(
-                f"Running command: {' '.join(command)} in cwd={self.output_dir}"
-            )
+            self.logger.debug(f"Running command: {' '.join(command)} in cwd={self.output_dir}")
+
             result = subprocess.run(
                 command,
                 input=json_data,
@@ -144,14 +168,34 @@ def run_smccnet(self, serialized_data: Dict[str, Any]) -> None:
             )
             self.logger.info(f"SMCCNET R script output:\n{result.stdout}")
             if result.stderr:
-                self.logger.warning(
-                    f"SMCCNET R script warnings/errors:\n{result.stderr}"
-                )
+                self.logger.warning(f"SMCCNET R script warnings/errors:\n{result.stderr}")
         except subprocess.CalledProcessError as e:
             self.logger.error(f"R script execution failed: {e.stderr}")
             raise
         except Exception as e:
-            self.logger.error(f"Error during SmCCNet execution: {e}")
+            self.logger.error(f"Error during SmCCNet execution: {e}\n")
+            raise
+
+    def get_clusters(self) -> list[pd.DataFrame, Any]:
+        """
+        Retrieves the subnetwork clusters generated by SmCCNet.
+
+        Returns:
+            list[pd.DataFrame, Any]: A list containing the cluster DataFrame and the cluster summary.
+        """
+        try:
+            clusters_path = Path(self.output_dir)
+            clusters_names = list(clusters_path.glob("size_*.csv"))
+            clusters = []
+            for cluster in clusters_names:
+                cluster_path = Path(self.output_dir / cluster)
+                cluster_df = pd.read_csv(cluster_path, index_col=0)
+                clusters.append(cluster_df)
+
+            self.logger.info(f"Found {len(clusters)} clusters in {self.output_dir}.")
+            return clusters[::-1]
+        except Exception as e:
+            self.logger.error(f"Error reading cluster summary: {e}")
             raise
 
     def run(self) -> pd.DataFrame:
@@ -165,11 +209,14 @@ def run(self) -> pd.DataFrame:
             self.logger.info("Starting SmCCNet workflow.")
             serialized_data = self.preprocess_data()
             self.run_smccnet(serialized_data)
-            adjacency_path = Path(self.output_dir) / "AdjacencyMatrix.csv"
-            self.logger.info(f"Reading adjacency matrix from: {adjacency_path}")
+            adjacency_path = Path(self.output_dir) / "GlobalNetwork.csv"
+            self.logger.info(f"Reading Global Network from: {adjacency_path}")
             adjacency_df = pd.read_csv(adjacency_path, index_col=0)
-            self.logger.info(f"Adjacency matrix shape: {adjacency_df.shape}")
-            return adjacency_df
+            self.logger.info(f"Global Network shape: {adjacency_df.shape}")
+            clusters = self.get_clusters()
+            self.logger.info("GlobalNetwork stored at index 0 and clusters stored as a list of dataframes at index 1.")
+            self.logger.info("SmCCNet workflow completed successfully.")
+            return adjacency_df, clusters
         except Exception as e:
             self.logger.error(f"Error in SmCCNet workflow: {e}")
             raise
diff --git a/bioneuralnet/external_tools/static_visualization.py b/bioneuralnet/external_tools/static_visualization.py
deleted file mode 100644
index c36b09e..0000000
--- a/bioneuralnet/external_tools/static_visualization.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import os
-from typing import Optional
-import pandas as pd
-import networkx as nx
-import matplotlib.pyplot as plt
-from ..utils.logger import get_logger
-
-
-class StaticVisualizer:
-    """
-    StaticVisualizer Class for Generating Static Network Visualizations.
-
-    Utilizes NetworkX and Matplotlib to create and save static images of networks.
-    """
-
-    def __init__(
-        self,
-        adjacency_matrix: pd.DataFrame,
-        layout: str = "spring",
-        node_size: int = 300,
-        node_color: str = "skyblue",
-        edge_color: str = "gray",
-        linewidths: float = 1.0,
-        font_size: int = 10,
-        output_dir: Optional[str] = None,
-        output_filename: str = "static_network.png",
-    ):
-        """
-        Initializes the StaticVisualizer instance.
-
-        Args:
-            adjacency_matrix (pd.DataFrame): Adjacency matrix representing the network.
-            layout (str, optional): Layout algorithm for network visualization ('spring', 'kamada_kawai', 'circular', etc.). Defaults to 'spring'.
-            node_size (int, optional): Size of the nodes in the visualization. Defaults to 300.
-            node_color (str, optional): Color of the nodes. Defaults to 'skyblue'.
-            edge_color (str, optional): Color of the edges. Defaults to 'gray'.
-            linewidths (float, optional): Width of the edges. Defaults to 1.0.
-            font_size (int, optional): Font size for node labels. Defaults to 10.
-            output_dir (str, optional): Directory to save the visualization image. If None, saves in the current directory. Defaults to None.
-            output_filename (str, optional): Filename for the saved visualization image. Defaults to "static_network.png".
-        """
-        self.adjacency_matrix = adjacency_matrix
-        self.layout = layout
-        self.node_size = node_size
-        self.node_color = node_color
-        self.edge_color = edge_color
-        self.linewidths = linewidths
-        self.font_size = font_size
-        self.output_dir = output_dir if output_dir else os.getcwd()
-        self.output_filename = output_filename
-        self.logger = get_logger(__name__)
-        self.logger.info("Initialized StaticVisualizer.")
-
-    def generate_graph(self) -> nx.Graph:
-        """
-        Converts the adjacency matrix into a NetworkX graph.
-
-        Returns:
-            nx.Graph: NetworkX graph constructed from the adjacency matrix.
-
-        Raises:
-            nx.NetworkXError: If the generated graph is empty.
-        """
-        self.logger.info("Generating NetworkX graph from adjacency matrix.")
-        G = nx.from_pandas_adjacency(self.adjacency_matrix)
-        self.logger.info(
-            f"Graph generated with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges."
-        )
-
-        if G.number_of_nodes() == 0:
-            self.logger.error("Generated graph is empty.")
-            raise nx.NetworkXError("Generated graph is empty.")
-
-        return G
-
-    def visualize(self, G: nx.Graph):
-        """
-        Generates and saves a static visualization of the network.
-
-        Args:
-            G (nx.Graph): NetworkX graph to visualize.
-        """
-        self.logger.info(f"Generating static visualization with layout: {self.layout}")
-
-        if self.layout == "spring":
-            pos = nx.spring_layout(G)
-        elif self.layout == "kamada_kawai":
-            pos = nx.kamada_kawai_layout(G)
-        elif self.layout == "circular":
-            pos = nx.circular_layout(G)
-        elif self.layout == "random":
-            pos = nx.random_layout(G)
-        elif self.layout == "shell":
-            pos = nx.shell_layout(G)
-        else:
-            self.logger.warning(
-                f"Layout '{self.layout}' not recognized. Falling back to spring layout."
-            )
-            pos = nx.spring_layout(G)
-
-        plt.figure(figsize=(12, 12))
-        nx.draw_networkx_nodes(
-            G, pos, node_size=self.node_size, node_color=self.node_color, alpha=0.7
-        )
-        nx.draw_networkx_edges(
-            G, pos, edge_color=self.edge_color, width=self.linewidths, alpha=0.5
-        )
-        nx.draw_networkx_labels(G, pos, font_size=self.font_size, font_color="black")
-        plt.title("Static Network Visualization", fontsize=15)
-        plt.axis("off")
-
-        os.makedirs(self.output_dir, exist_ok=True)
-        output_path = os.path.join(self.output_dir, self.output_filename)
-
-        plt.tight_layout()
-        plt.savefig(output_path, format="PNG")
-        plt.close()
-        self.logger.info(f"Static network visualization saved to {output_path}")
diff --git a/bioneuralnet/metrics/__init__.py b/bioneuralnet/metrics/__init__.py
index 3703e67..3fa4c72 100644
--- a/bioneuralnet/metrics/__init__.py
+++ b/bioneuralnet/metrics/__init__.py
@@ -1,7 +1,4 @@
-from .correlation import Correlation
-from .mse import MSE
-from .precision import Precision
-from .recall import Recall
-from .f1_score import F1Score
+from .check_variance import CheckVariance, network_remove_low_variance, network_remove_high_zero_fraction
+from .correlation import compute_correlation, compute_cluster_correlation_from_df,convert_louvain_to_adjacency
 
-__all__ = ["Correlation", "MSE", "Precision", "Recall", "F1Score"]
+__all__ = ["CheckVariance", "compute_correlation", "compute_cluster_correlation_from_df", "network_remove_low_variance", "network_remove_high_zero_fraction", "convert_louvain_to_adjacency"]
\ No newline at end of file
diff --git a/bioneuralnet/metrics/check_variance.py b/bioneuralnet/metrics/check_variance.py
new file mode 100644
index 0000000..698b291
--- /dev/null
+++ b/bioneuralnet/metrics/check_variance.py
@@ -0,0 +1,96 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from ..utils.logger import get_logger
+
+class CheckVariance:
+    def __init__(self, dataframe: pd.DataFrame):
+        """
+        Initialize with the dataframe whose feature variances you want to analyze.
+        
+        :param dataframe: A pandas DataFrame containing the data.
+        """
+        self.df = dataframe
+        self.logger = get_logger(__name__)
+        self.logger.info(f"CheckVariance initialized with dataframe of shape {self.df.shape}")
+
+    def plot_variance_distribution(self, bins: int = 50):
+        """
+        Compute the variance for each feature (column) in the DataFrame and plot
+        a histogram of these variances.
+
+        :param bins: Number of bins for the histogram.
+        :return: A matplotlib Figure object containing the plot.
+        """
+        variances = self.df.var()
+        self.logger.info("Computed variances for each feature.")
+
+        fig, ax = plt.subplots(figsize=(8, 6))
+        ax.hist(variances, bins=bins, edgecolor='black')
+        ax.set_title("Distribution of Feature Variances")
+        ax.set_xlabel("Variance")
+        ax.set_ylabel("Frequency")
+        
+        self.logger.info("Variance distribution plot generated.")
+        return fig
+
+    def plot_variance_by_feature(self):
+        """
+        Plot the variance for each feature against its index or name.
+        
+        :return: A matplotlib Figure object containing the plot.
+        """
+        variances = self.df.var()
+        self.logger.info("Computed variances for each feature for index plot.")
+
+        fig, ax = plt.subplots(figsize=(10, 6))
+        ax.plot(variances.index, variances.values, 'o', markersize=4)
+        ax.set_title("Variance per Feature")
+        ax.set_xlabel("Feature")
+        ax.set_ylabel("Variance")
+        ax.tick_params(axis='x', rotation=90)
+        
+        self.logger.info("Variance vs. feature index plot generated.")
+        return fig
+
+    def remove_near_zero_variance(self,variance_threshold=1e-6):
+        self.logger.info()
+        variances = self.df.var()
+        return self.df.loc[:, variances > variance_threshold]
+
+    def remove_high_zero_fraction(self, zero_frac_threshold=0.95):
+        zero_fraction = (self.df == 0).sum(axis=0) / self.df.shape[0]
+        return self.df.loc[:, zero_fraction < zero_frac_threshold]
+
+def network_remove_low_variance(network, threshold=1e-6):
+    """
+    Removes rows and columns from a symmetric adjacency matrix 
+    where the variance is below a specified threshold.
+    
+    Parameters:
+        network (pd.DataFrame): The input symmetric adjacency matrix.
+        threshold (float): Variance threshold below which rows/columns are removed.
+    
+    Returns:
+        pd.DataFrame: The filtered adjacency matrix.
+    """
+    variances = network.var(axis=0)
+    valid_indices = variances[variances > threshold].index
+    return network.loc[valid_indices, valid_indices]
+
+def network_remove_high_zero_fraction(network, threshold=0.95):
+    """
+    Removes rows and columns from a symmetric adjacency matrix 
+    where the fraction of zero entries is higher than a specified threshold.
+    
+    Parameters:
+        network (pd.DataFrame): The input symmetric adjacency matrix.
+        threshold (float): Zero-fraction threshold above which rows/columns are removed.
+    
+    Returns:
+        pd.DataFrame: The filtered adjacency matrix.
+    """
+    zero_fraction = (network == 0).sum(axis=0) / network.shape[0]
+    valid_indices = zero_fraction[zero_fraction < threshold].index
+    return network.loc[valid_indices, valid_indices]
+
+
diff --git a/bioneuralnet/metrics/correlation.py b/bioneuralnet/metrics/correlation.py
index 474e8c4..7b9929d 100644
--- a/bioneuralnet/metrics/correlation.py
+++ b/bioneuralnet/metrics/correlation.py
@@ -1,32 +1,68 @@
 import pandas as pd
-from ..utils.logger import get_logger
+from sklearn.decomposition import PCA
+from scipy.stats import pearsonr
+from bioneuralnet.utils.logger import get_logger
+import numpy as np
 
+logger = get_logger(__name__)
 
-class Correlation:
+def compute_correlation(predictions: pd.Series, targets: pd.Series) -> float:
     """
-    Computes the Pearson correlation coefficient between predictions and targets.
+    Compute the Pearson correlation coefficient between predictions and targets.
     """
+    logger.info("Computing Pearson correlation coefficient.")
+    if predictions.empty or targets.empty:
+        logger.error("Predictions and targets must not be empty.")
+        raise ValueError("Predictions and targets must not be empty.")
+    if len(predictions) != len(targets):
+        logger.error("Predictions and targets must have the same length.")
+        raise ValueError("Predictions and targets must have the same length.")
+    correlation = predictions.corr(targets)
+    logger.info(f"Pearson correlation coefficient: {correlation}")
+    return correlation if not pd.isna(correlation) else 0.0
 
-    def __init__(self):
-        self.logger = get_logger(__name__)
-
-    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
-        """
-        Compute the Pearson correlation coefficient between predictions and targets.
-        Parameters
-        Returns
-        Raises
-        """
-        self.logger.info("Computing Pearson correlation coefficient.")
-
-        if predictions.empty or targets.empty:
-            self.logger.error("Predictions and targets must not be empty.")
-            raise ValueError("Predictions and targets must not be empty.")
-
-        if len(predictions) != len(targets):
-            self.logger.error("Predictions and targets must have the same length.")
-            raise ValueError("Predictions and targets must have the same length.")
-
-        correlation = predictions.corr(targets)
-        self.logger.info(f"Pearson correlation coefficient: {correlation}")
-        return correlation if not pd.isna(correlation) else 0.0
+
+def compute_cluster_correlation_from_df(cluster_df: pd.DataFrame, pheno: pd.DataFrame) -> tuple:
+    """
+    Compute the Pearson correlation coefficient between PC1 of a cluster and phenotype.
+    
+    Returns:
+        (cluster_size, correlation) or (size, None) if correlation fails.
+    """
+    cluster_size = cluster_df.shape[1]
+
+    if cluster_size < 2:
+        logger.info(f"Cluster with size {cluster_size} skipped (not enough features).")
+        return (cluster_size, None)
+
+    subset = cluster_df.fillna(0)
+    
+    if subset.var().sum() == 0:
+        logger.warning("Cluster skipped: all features have zero variance.")
+        return (cluster_size, None)
+
+    try:
+        pca = PCA(n_components=1)
+        pc1 = pca.fit_transform(subset)
+        pc1_series = pd.Series(pc1.flatten(), index=subset.index, name="PC1")
+
+        pheno_series = pheno.iloc[:, 0]  
+        pc1_series, pheno_series = pc1_series.align(pheno_series, join="inner")
+
+        if len(pc1_series) < 3:
+            logger.warning("Not enough data points for Pearson correlation.")
+            return (cluster_size, None)
+
+        corr, _ = pearsonr(pc1_series, pheno_series)
+
+    except Exception as e:
+        logger.error(f"Error computing correlation: {e}")
+        corr = None
+
+    return (cluster_size, corr)
+
+def convert_louvain_to_adjacency(louvain_cluster: pd.DataFrame) -> pd.DataFrame:
+    adjacency_matrix = louvain_cluster.corr(method="pearson")
+    np.fill_diagonal(adjacency_matrix.values, 0)
+    adjacency_matrix = adjacency_matrix.fillna(0)
+    return adjacency_matrix
diff --git a/bioneuralnet/metrics/f1_score.py b/bioneuralnet/metrics/f1_score.py
deleted file mode 100644
index 2b5b1a9..0000000
--- a/bioneuralnet/metrics/f1_score.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import pandas as pd
-from ..utils.logger import get_logger
-
-
-class F1Score:
-    """
-    F1 Score Metric Class.
-
-    Computes the F1 score between predictions and targets.
-    Suitable for classification tasks.
-    """
-
-    def __init__(self, average: str = "binary"):
-        """
-        Initialize the F1 Score metric.
-        """
-        self.logger = get_logger(__name__)
-        self.average = average
-
-    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
-        """
-        Compute the F1 score between predictions and targets.
-        Parameter
-        Returns
-        Raises
-        """
-        self.logger.info(f"Computing F1 Score with average='{self.average}'.")
-
-        if predictions.empty or targets.empty:
-            self.logger.error("Predictions and targets must not be empty.")
-            raise ValueError("Predictions and targets must not be empty.")
-
-        if len(predictions) != len(targets):
-            self.logger.error("Predictions and targets must have the same length.")
-            raise ValueError("Predictions and targets must have the same length.")
-
-        if self.average != "binary":
-            self.logger.error(
-                "Only 'binary' average is supported without scikit-learn."
-            )
-            raise NotImplementedError("Only 'binary' average is supported.")
-
-        tp = ((predictions == 1) & (targets == 1)).sum()
-        fp = ((predictions == 1) & (targets == 0)).sum()
-        fn = ((predictions == 0) & (targets == 1)).sum()
-
-        precision = float(tp) / float(tp + fp) if (tp + fp) > 0 else 0.0
-        recall = float(tp) / float(tp + fn) if (tp + fn) > 0 else 0.0
-
-        if precision + recall == 0:
-            f1 = 0.0
-        else:
-            f1 = 2.0 * (precision * recall) / (precision + recall)
-
-        self.logger.info(f"F1 Score: {f1}")
-        return f1
diff --git a/bioneuralnet/metrics/mse.py b/bioneuralnet/metrics/mse.py
deleted file mode 100644
index 3ecaa12..0000000
--- a/bioneuralnet/metrics/mse.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import pandas as pd
-from ..utils.logger import get_logger
-
-
-class MSE:
-    """
-    Computes the mean squared error between predictions and targets.
-    """
-
-    def __init__(self):
-        self.logger = get_logger(__name__)
-
-    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
-        """
-        Compute the mean squared error between predictions and targets.
-        Parameters
-        Returns
-        Raises
-        """
-        self.logger.info("Computing Mean Squared Error (MSE).")
-
-        if predictions.empty or targets.empty:
-            self.logger.error("Predictions and targets must not be empty.")
-            raise ValueError("Predictions and targets must not be empty.")
-
-        if len(predictions) != len(targets):
-            self.logger.error("Predictions and targets must have the same length.")
-            raise ValueError("Predictions and targets must have the same length.")
-
-        mse = ((predictions - targets) ** 2).mean()
-        self.logger.info(f"Mean Squared Error (MSE): {mse}")
-        return mse
diff --git a/bioneuralnet/metrics/precision.py b/bioneuralnet/metrics/precision.py
deleted file mode 100644
index 180df27..0000000
--- a/bioneuralnet/metrics/precision.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import pandas as pd
-from ..utils.logger import get_logger
-
-
-class Precision:
-    """
-    Computes the precision score between predictions and targets.
-    for classification tasks.
-    """
-
-    def __init__(self, average: str = "binary"):
-        """
-        Initialize the Precision metric.
-        """
-        self.logger = get_logger(__name__)
-        self.average = average
-
-    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
-        """
-        Compute the precision score between predictions and targets.
-        Parameters
-        Returns
-        Raises
-        """
-        self.logger.info(f"Computing Precision with average='{self.average}'.")
-
-        if predictions.empty or targets.empty:
-            self.logger.error("Predictions and targets must not be empty.")
-            raise ValueError("Predictions and targets must not be empty.")
-
-        if len(predictions) != len(targets):
-            self.logger.error("Predictions and targets must have the same length.")
-            raise ValueError("Predictions and targets must have the same length.")
-
-        if self.average != "binary":
-            self.logger.error(
-                "Only 'binary' average is supported without scikit-learn."
-            )
-            raise NotImplementedError("Only 'binary' average is supported.")
-
-        tp = ((predictions == 1) & (targets == 1)).sum()
-        fp = ((predictions == 1) & (targets == 0)).sum()
-
-        precision = float(tp) / float(tp + fp) if (tp + fp) > 0 else 0.0
-        self.logger.info(f"Precision Score: {precision}")
-        return precision
diff --git a/bioneuralnet/metrics/recall.py b/bioneuralnet/metrics/recall.py
deleted file mode 100644
index ef16e57..0000000
--- a/bioneuralnet/metrics/recall.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import pandas as pd
-from ..utils.logger import get_logger
-
-
-class Recall:
-    """
-    Computes the recall score between predictions and targets.
-    for classification tasks.
-    """
-
-    def __init__(self, average: str = "binary"):
-        """
-        Initialize the Recall metric.
-        """
-        self.logger = get_logger(__name__)
-        self.average = average
-
-    def compute(self, predictions: pd.Series, targets: pd.Series) -> float:
-        """
-        Compute the recall score between predictions and targets.
-        Parameters
-        Returns
-        Raises
-        """
-        self.logger.info(f"Computing Recall with average='{self.average}'.")
-
-        if predictions.empty or targets.empty:
-            self.logger.error("Predictions and targets must not be empty.")
-            raise ValueError("Predictions and targets must not be empty.")
-
-        if len(predictions) != len(targets):
-            self.logger.error("Predictions and targets must have the same length.")
-            raise ValueError("Predictions and targets must have the same length.")
-
-        if self.average != "binary":
-            self.logger.error(
-                "Only 'binary' average is supported without scikit-learn."
-            )
-            raise NotImplementedError("Only 'binary' average is supported.")
-
-        tp = ((predictions == 1) & (targets == 1)).sum()
-        fn = ((predictions == 0) & (targets == 1)).sum()
-
-        recall = float(tp) / float(tp + fn) if (tp + fn) > 0 else 0.0
-        self.logger.info(f"Recall Score: {recall}")
-        return recall
diff --git a/bioneuralnet/network_embedding/gnn_embedding.py b/bioneuralnet/network_embedding/gnn_embedding.py
index d4c65c3..047dab5 100644
--- a/bioneuralnet/network_embedding/gnn_embedding.py
+++ b/bioneuralnet/network_embedding/gnn_embedding.py
@@ -1,4 +1,4 @@
-from typing import Dict, Optional
+from typing import Optional, Union
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -20,6 +20,7 @@
 from sklearn.metrics import accuracy_score
 
 import os
+import json
 
 
 class GNNEmbedding:
@@ -36,13 +37,12 @@ def __init__(
         phenotype_col: str = "phenotype",
         model_type: str = "GAT",
         hidden_dim: int = 64,
-        layer_num: int = 2,
+        layer_num: int = 4,
         dropout: bool = True,
         num_epochs: int = 100,
         lr: float = 1e-3,
         weight_decay: float = 1e-4,
         gpu: bool = False,
-        random_feature_dim: int = 16,
         seed: Optional[int] = None,
         tune: Optional[bool] = False,
     ):
@@ -63,7 +63,6 @@ def __init__(
             lr : float, optional
             weight_decay : float, optional
             gpu : bool, optional
-            random_feature_dim : int, optional
             seed : Optional[int], default=None
         """
         self.logger = get_logger(__name__)
@@ -80,10 +79,7 @@ def __init__(
             raise ValueError(f"Phenotype data must have column '{phenotype_col}'.")
         if clinical_data is not None and clinical_data.empty:
             raise ValueError("Clinical data cannot be empty.")
-            # self.logger.warning(
-            #     "Clinical data provided is empty ... using random features."
-            # )
-            # clinical_data = None
+    
 
         self.adjacency_matrix = adjacency_matrix
         self.omics_data = omics_data
@@ -98,7 +94,6 @@ def __init__(
         self.num_epochs = num_epochs
         self.lr = lr
         self.weight_decay = weight_decay
-        self.random_feature_dim = random_feature_dim
 
         self.device = torch.device(
             "cuda" if gpu and torch.cuda.is_available() else "cpu"
@@ -126,39 +121,58 @@ def fit(self) -> None:
             self.logger.error(f"Error during training: {e}")
             raise
 
-    def embed(self) -> torch.Tensor:
-        """
-        Generates and retrieves node embeddings from the trained GNN model.
-
-        Returns:
-            torch.Tensor
-
-        Raises:
-            ValueError
-        """
+    def embed(self, as_df: bool = False) -> Union[torch.Tensor, pd.DataFrame]:
         self.logger.info("Generating node embeddings.")
         if self.tune:
-            self.logger.info(
-                "Tuning is enabled. Running hyperparameter tuning for GNNEmbedding."
-            )
+            self.logger.info("Tuning is enabled. Running hyperparameter tuning.")
             best_config = self.run_gnn_embedding_tuning(num_samples=5)
             self.logger.info(f"Best tuning config: {best_config}")
-
         if self.model is None or self.data is None:
-            self.logger.error(
-                "Model has not been trained. Call 'fit()' before 'embed()'."
-            )
-            raise ValueError(
-                "Model has not been trained. Call 'fit()' before 'embed()'."
-            )
+            self.logger.error("Model has not been trained. Call 'fit()' before 'embed()'.")
+            raise ValueError("Model has not been trained. Call 'fit()' before 'embed()'.")
         try:
             self.embeddings = self._generate_embeddings(self.model, self.data)
             self.logger.info("Node embeddings generated successfully.")
-            return self.embeddings
+            if as_df:
+                embeddings_df = self._tensor_to_df(self.embeddings, self.adjacency_matrix)
+                return embeddings_df
+            else:
+                return self.embeddings
         except Exception as e:
             self.logger.error(f"Error during embedding generation: {e}")
             raise
 
+
+    def _tensor_to_df(self, embeddings_tensor: torch.Tensor, network: pd.DataFrame) -> pd.DataFrame:
+        """
+        Convert embeddings tensor to DataFrame with node (feature) names as the index,
+        and embedding dimension labels as columns.
+        """
+        try:
+            self.logger.info("Converting embeddings tensor to DataFrame.")
+            if embeddings_tensor is None:
+                raise ValueError("Embeddings tensor is empty (None).")
+            if network is None:
+                raise ValueError("Network (adjacency matrix) is empty (None).")
+
+            if embeddings_tensor.shape[0] != len(network.index):
+                raise ValueError(
+                    f"Mismatch: embeddings tensor has {embeddings_tensor.shape[0]} rows, "
+                    f"but network index has {len(network.index)} rows."
+                )
+            self.logger.debug(f"Embeddings tensor shape: {embeddings_tensor.shape}")
+
+            embeddings_df = pd.DataFrame(
+                embeddings_tensor.numpy(),
+                index=network.index,
+                columns=[f"Embed_{i+1}" for i in range(embeddings_tensor.shape[1])]
+            )
+            return embeddings_df
+
+        except Exception as e:
+            self.logger.error(f"Error during conversion: {e}")
+            raise
+        
     def _prepare_node_features(self) -> pd.DataFrame:
         """
         Build node feature vector by computing, for each omics feature (node) in the network,
@@ -181,12 +195,18 @@ def _prepare_node_features(self) -> pd.DataFrame:
             raise ValueError(
                 "No common features found between the network and omics data."
             )
+        if len(common_features) != len(network_features):
+            raise ValueError(
+                "Ther are duplicate features in the network or omics data."
+            )
         self.logger.info(
             f"Found {len(common_features)} common features between network and omics data."
         )
 
+        omics_in_network = self.omics_data[self.adjacency_matrix.columns]
+
         self.adjacency_matrix = self.adjacency_matrix.loc[
-            common_features, common_features
+            omics_in_network.columns, omics_in_network.columns
         ]
 
         clinical_cols = (
@@ -220,7 +240,7 @@ def _prepare_node_labels(self) -> pd.Series:
             f"Preparing node labels by correlating each omics feature with phenotype column '{self.phenotype_col}'."
         )
         common_samples = self.omics_data.index.intersection(self.phenotype_data.index)
-        print(common_samples)
+        self.logger.info(common_samples)
 
         if len(common_samples) == 0:
             raise ValueError("No common samples between omics data and phenotype data.")
@@ -339,6 +359,7 @@ def _generate_embeddings(self, model: nn.Module, data: Data) -> torch.Tensor:
         """
         self.logger.info("Generating node embeddings from the trained GNN model.")
         model.eval()
+        
         data = data.to(self.device)
         with torch.no_grad():
             embeddings = model.get_embeddings(data)
@@ -359,7 +380,6 @@ def _tune_helper(self, config):
             lr=config["lr"],
             weight_decay=config["weight_decay"],
             gpu=(self.device.type == "cuda"),
-            random_feature_dim=self.random_feature_dim,
             seed=None,
             tune=False,
         )
@@ -368,7 +388,6 @@ def _tune_helper(self, config):
 
         node_labels = self._prepare_node_labels().values
         y = (node_labels > node_labels.mean()).astype(int)
-
         X = node_emb.detach().numpy()
 
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
@@ -387,15 +406,15 @@ def run_gnn_embedding_tuning(self, num_samples=10):
         config = {
             "model_type": tune.choice(["GCN", "GAT", "GAT", "SAGE", "GIN"]),
             "hidden_dim": tune.choice([8, 16, 32, 64, 128]),
-            "layer_num": tune.choice([2, 3, 4, 5]),
+            "layer_num": tune.choice([2, 3, 4, 5, 6]),
             "dropout": tune.choice([True, False]),
-            "num_epochs": tune.choice([50, 100, 150, 200]),
+            "num_epochs": tune.choice([16, 64, 256, 512, 1024]),
             "lr": tune.loguniform(1e-4, 1e-1),
             "weight_decay": tune.loguniform(1e-4, 1e-1),
         }
 
         scheduler = ASHAScheduler(
-            metric="accuracy", mode="max", grace_period=10, reduction_factor=2
+            metric="accuracy", mode="max", grace_period=1, reduction_factor=2
         )
         reporter = CLIReporter(metric_columns=["accuracy", "training_iteration"])
 
@@ -407,13 +426,20 @@ def short_dirname_creator(trial):
             config=config,
             num_samples=num_samples,
             scheduler=scheduler,
+            verbose=0,
             progress_reporter=reporter,
-            storage_path=os.path.expanduser("~/r"),
+            storage_path=os.path.expanduser("~/gnn"),
             trial_dirname_creator=short_dirname_creator,
             name="e",
         )
 
         best_trial = result.get_best_trial("accuracy", "max", "last")
-        print("Best trial config:", best_trial.config)
-        print("Best trial final accuracy:", best_trial.last_result["accuracy"])
+        self.logger.info(f"Best trial config: {best_trial.config}")
+        self.logger.info(f"Best trial final accuracy: {best_trial.last_result['accuracy']}")
+        
+        best_params_file = "embeddings_best_params.json"
+        with open(best_params_file, "w") as f:
+            json.dump(best_trial.config, f, indent=4)
+        self.logger.info(f"Best embedding parameters saved to {best_params_file}")
+        
         return best_trial.config
diff --git a/bioneuralnet/subject_representation/subject_representation.py b/bioneuralnet/subject_representation/subject_representation.py
index e4ce68b..0753748 100644
--- a/bioneuralnet/subject_representation/subject_representation.py
+++ b/bioneuralnet/subject_representation/subject_representation.py
@@ -14,7 +14,7 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score
 from ray.air import session
-
+import json
 
 class GraphEmbedding:
     """
@@ -45,24 +45,17 @@ def __init__(
             raise ValueError("Omics data must be non-empty.")
 
         if embeddings is None or embeddings.empty:
-            self.embedding_method = "GNNs"
             self.logger.info(
                 "No embeddings provided, please review documentation to see how to generate embeddings."
             )
-        else:
-            self.embedding_method = "precomputed"
-            if not isinstance(embeddings, pd.DataFrame):
-                raise ValueError("Embeddings must be provided as a pandas DataFrame.")
-
-            self.logger.info(
-                "Precomputed embeddings provided. Skipping GNN-based embedding generation."
-            )
-
+        if not isinstance(embeddings, pd.DataFrame):
+            raise ValueError("Embeddings must be provided as a pandas DataFrame.")
+        
         if tune == True and phenotype_data is None:
             raise ValueError(
                 "Phenotype data must be provided for classification-based tuning."
             )
-
+        
         self.omics_data = omics_data
         self.embeddings = embeddings if embeddings is not None else pd.DataFrame()
         self.phenotype_data = phenotype_data
@@ -70,6 +63,23 @@ def __init__(
         self.reduce_method = reduce_method.upper()
         self.tune = tune
 
+        embeddings_features = set(self.embeddings.index)
+        omics_features = set(self.omics_data.columns)
+        if len(embeddings_features) != len(omics_features):
+            raise ValueError(
+                f"Number of features in embeddings and omics data do not match.\n"
+                f"Embeddings: {self.embeddings.shape} and Omics: {self.omics_data.shape}"
+            )
+        common_features = embeddings_features.intersection(omics_features)
+        if len(common_features) == 0:
+            raise ValueError(
+                f"No common features found between the embeddings and omics data.\n"
+                f"Embeddings: {self.embeddings.shape} and Omics: {self.omics_data.shape}"
+            )
+        self.logger.info(
+            f"Found {len(common_features)} common features between network and omics data."
+        )
+
     def run(self) -> pd.DataFrame:
         """
         If tune=True, perform classification-based tuning (if phenotype_data provided),
@@ -79,7 +89,7 @@ def run(self) -> pd.DataFrame:
 
         if self.embeddings.empty:
             self.logger.warning(
-                "No embeddings provided. Returning original omics_data."
+                "No embeddings provided. Please generate emebeddings using GNNEmbeddings class.\nReturning original omics_data."
             )
             return self.omics_data
 
@@ -98,8 +108,7 @@ def run(self) -> pd.DataFrame:
             else:
                 reduced = self._reduce_embeddings(
                     method=self.reduce_method,
-                    pca_dim=1,
-                    ae_params={"epochs": 16, "hidden_dim": 8},
+                    pca_dim=2,
                 )
 
             if reduced.empty:
@@ -119,15 +128,19 @@ def run(self) -> pd.DataFrame:
             raise
 
     def _reduce_embeddings(
-        self, method: str, pca_dim: int = 1, ae_params: Dict[str, Any] = None
+        self, method: str, pca_dim: int = 2, ae_params: Dict[str, Any] = None
     ) -> pd.Series:
-        self.logger.info(f"Reducing embeddings to 1D using method='{method}'.")
+        
+        self.logger.info(f"Reducing embeddings to {pca_dim} using method='{method}'.")
+        
         if self.embeddings.empty:
             raise ValueError("Embeddings DataFrame is empty.")
+        
         if method == "PCA":
             self.logger.info(f"Applying PCA with n_components={pca_dim}.")
             pca = PCA(n_components=pca_dim)
             pcs = pca.fit_transform(self.embeddings)
+            
             if pca_dim == 1:
                 reduced_embedding = pd.Series(
                     pcs.flatten(), index=self.embeddings.index, name="PC1"
@@ -140,34 +153,30 @@ def _reduce_embeddings(
                 "Captured variance ratio: %.2f" % pca.explained_variance_ratio_[0]
             )
             self.logger.info("PCA reduction completed.")
-        # elif method == "AVG":
-        #     self.logger.info("Calculating average of embedding dimensions.")
-        #     reduced_embedding = self.embeddings.mean(axis=1).rename("Avg_Embed")
-        #     self.logger.info("Average reduction completed.")
-        # elif method == "MAX":
-        #     self.logger.info("Calculating maximum of embedding dimensions.")
-        #     reduced_embedding = self.embeddings.max(axis=1).rename("Max_Embed")
-        #     self.logger.info("Maximum reduction completed.")
+
         elif method == "AE":
             self.logger.info("Using Autoencoder for reduction.")
             if ae_params is None:
-                ae_params = {"epochs": 16, "hidden_dim": 8}
+                ae_params = {"epochs": 16, "hidden_dim": 8, "lr": 1e-3}
             input_dim = self.embeddings.shape[1]
             X = torch.tensor(self.embeddings.values, dtype=torch.float)
             model = SimpleAE(
                 input_dim=input_dim,
-                hidden_dim=ae_params.get("hidden_dim", 16),
+                hidden_dim=ae_params.get("hidden_dim", 8),
                 compressed_dim=1,
             )
-            optimizer = optim.Adam(model.parameters(), lr=1e-3)
+            optimizer = optim.Adam(model.parameters(), lr=ae_params.get("lr", 1e-3))
             loss_fn = nn.MSELoss()
             model.train()
-            for epoch in range(ae_params.get("epochs", 10)):
+            epochs = ae_params.get("epochs", 16)
+            for epoch in range(epochs):
                 optimizer.zero_grad()
                 z, recon = model(X)
                 loss = loss_fn(recon, X)
                 loss.backward()
                 optimizer.step()
+                if (epoch + 1) % max(1, epochs // 5) == 0:
+                    self.logger.info(f"AE Epoch {epoch+1}/{epochs} - Loss: {loss.item():.4f}")
             model.eval()
             with torch.no_grad():
                 z, _ = model(X)
@@ -182,7 +191,6 @@ def _reduce_embeddings(
         reduced_embedding = (reduced_embedding - reduced_embedding.mean()) / (
             reduced_embedding.std() + 1e-8
         )
-
         self.logger.info("Reduced embedding normalized.")
         return reduced_embedding
 
@@ -204,7 +212,6 @@ def _integrate_embeddings(
             self.logger.info(
                 "Integration using multiplication completed. (Columns overwritten)"
             )
-
         elif method == "concatenate":
             enhanced_features = pd.DataFrame(
                 np.tile(reduced.values, (self.omics_data.shape[0], 1)),
@@ -221,7 +228,6 @@ def _integrate_embeddings(
                     enhanced_omics[feature] + weight * reduced[feature]
                 )
             self.logger.info("Integration using weighted sum completed.")
-
         else:
             raise ValueError(f"Unknown integration method: {method}")
 
@@ -242,9 +248,12 @@ def _run_classification_tuning(self) -> Dict[str, Any]:
             "pca_dim": tune.choice([1, 2, 3]),
             "ae_params": tune.choice(
                 [
-                    {"epochs": 64, "hidden_dim": 8},
-                    {"epochs": 128, "hidden_dim": 8},
+                    {"epochs": 64, "hidden_dim": 128},
+                    {"epochs": 128, "hidden_dim": 16},
+                    {"epochs": 256, "hidden_dim": 8},
+                    {"epochs": 128, "hidden_dim": 16},
                     {"epochs": 256, "hidden_dim": 4},
+                    {"epochs": 512, "hidden_dim": 4},
                 ]
             ),
             "integration_method": tune.choice(["multiply"]),
@@ -256,16 +265,13 @@ def tune_helper(config):
             )
             if config["integration_method"] == "multiply":
                 enhanced = self._integrate_embeddings(reduced, method="multiply")
-            # elif config["integration_method"] == "concatenate":
-            #     enhanced = self._integrate_embeddings(reduced, method="concatenate")
-            # elif config["integration_method"] == "weighted":
-            #     enhanced = self._integrate_embeddings(reduced, method="weighted")
             else:
                 raise ValueError("Unknown integration method")
 
             common_samples = enhanced.index.intersection(self.phenotype_data.index)
             X = enhanced.loc[common_samples].values
-            y = self.phenotype_data.loc[common_samples, self.phenotype_col].values
+            y = self.phenotype_data.loc[common_samples, self.phenotype_col].astype(int).values
+
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
             clf = RandomForestClassifier(n_estimators=100)
             clf.fit(X_train, y_train)
@@ -273,7 +279,7 @@ def tune_helper(config):
             acc = accuracy_score(y_test, y_pred)
             session.report({"accuracy": acc})
 
-        scheduler = ASHAScheduler(metric="accuracy", mode="max", grace_period=10)
+        scheduler = ASHAScheduler(metric="accuracy", mode="max", grace_period=1, reduction_factor=2)
         reporter = CLIReporter(metric_columns=["accuracy", "training_iteration"])
 
         def short_dirname_creator(trial):
@@ -283,30 +289,55 @@ def short_dirname_creator(trial):
             tune_helper,
             config=search_config,
             num_samples=10,
+            verbose=0,
             scheduler=scheduler,
             progress_reporter=reporter,
             trial_dirname_creator=short_dirname_creator,
             name="tune",
         )
+
         best_trial = analysis.get_best_trial("accuracy", "max", "last")
+        self.logger.info(f"Best trial config: {best_trial.config}")
+        self.logger.info(f"Best trial final accuracy: {best_trial.last_result['accuracy']}")
+
+        best_params_file = "Graph_embedding_best_params.json"
+        with open(best_params_file, "w") as f:
+            json.dump(best_trial.config, f, indent=4)
+        self.logger.info(f"Best Graph Embedding parameters saved to {best_params_file}")
+        
         return best_trial.config
 
 
 class SimpleAE(nn.Module):
-    def __init__(self, input_dim, hidden_dim=16, compressed_dim=1):
+    def __init__(self, input_dim, hidden_dim: int = 8, compressed_dim: int = 1):
+        """
+        Taking reference from DPMON, I modified SimpleAE class to deepen the network architecture.
+        
+        Parameters:
+            input_dim (int): Dimensionality of the input.
+            hidden_dim (int): Size of the first hidden layer. The second hidden layer is set to half this size.
+            compressed_dim (int): Dimensionality of the latent representation.
+        """
         super(SimpleAE, self).__init__()
+        hidden1 = hidden_dim
+        hidden2 = max(1, hidden_dim // 2)
         self.encoder = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim),
+            nn.Linear(input_dim, hidden1),
             nn.ReLU(),
-            nn.Linear(hidden_dim, compressed_dim),
+            nn.Linear(hidden1, hidden2),
+            nn.ReLU(),
+            nn.Linear(hidden2, compressed_dim),
         )
         self.decoder = nn.Sequential(
-            nn.Linear(compressed_dim, hidden_dim),
+            nn.Linear(compressed_dim, hidden2),
+            nn.ReLU(),
+            nn.Linear(hidden2, hidden1),
             nn.ReLU(),
-            nn.Linear(hidden_dim, input_dim),
+            nn.Linear(hidden1, input_dim),
         )
 
     def forward(self, x):
         z = self.encoder(x)
         recon = self.decoder(z)
         return z, recon
+
diff --git a/bioneuralnet/utils/__init__.py b/bioneuralnet/utils/__init__.py
index 459c9c5..30a63b5 100644
--- a/bioneuralnet/utils/__init__.py
+++ b/bioneuralnet/utils/__init__.py
@@ -1,5 +1,6 @@
 from .logger import get_logger
 from .path_utils import validate_paths
-from .rdata_to_csv import convert_rdata_to_csv
+from .rdata_to_csv import rdata_to_csv_file
+from .notebook_example import evaluate_rf_regressor,evaluate_rf_classifier,plot_embeddings, plot_network, plot_performance,compare_clusters
 
-__all__ = ["get_logger", "validate_paths", "convert_rdata_to_csv"]
+__all__ = ["get_logger", "validate_paths", "rdata_to_csv_file","evaluate_rf_regressor", "evaluate_rf_classifier", "plot_embeddings", "plot_network", "plot_performance", "compare_clusters"]
diff --git a/bioneuralnet/utils/convert_Rdata_to_csv.R b/bioneuralnet/utils/convert_Rdata_to_csv.R
deleted file mode 100644
index d5784fb..0000000
--- a/bioneuralnet/utils/convert_Rdata_to_csv.R
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env Rscript
-
-# check for arguments
-args <- commandArgs(trailingOnly = TRUE)
-if (length(args) != 2) {
-    stop("Usage: convert_Rdata_to_csv.R input.Rdata output.csv", call. = FALSE)
-}
-
-input_file <- args[1]
-output_file <- args[2]
-
-# load the Rdata file to the workspace
-load(input_file)
-
-if (exists("AdjacencyMatrix")) {
-    mat <- AdjacencyMatrix
-} else if (exists("M")) {
-    mat <- M
-} else {
-    stop("Neither 'AdjacencyMatrix' nor 'M' was found in the Rdata file.", call. = FALSE)
-}
-
-# write the matrix or data.frame to a csv file.
-write.csv(mat, file = output_file, row.names = TRUE, quote = FALSE)
diff --git a/bioneuralnet/utils/notebook_example.py b/bioneuralnet/utils/notebook_example.py
new file mode 100644
index 0000000..a5124a6
--- /dev/null
+++ b/bioneuralnet/utils/notebook_example.py
@@ -0,0 +1,301 @@
+import numpy as np
+import pandas as pd
+import networkx as nx
+import seaborn as sns
+from umap import UMAP
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+from sklearn.decomposition import PCA
+
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from sklearn.metrics import r2_score, accuracy_score
+from sklearn.model_selection import train_test_split
+from bioneuralnet.metrics.correlation import compute_cluster_correlation_from_df
+from bioneuralnet.utils import get_logger
+
+logger = get_logger(__name__)
+
+def evaluate_rf_regressor(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y)
+    rf = RandomForestRegressor(n_estimators=100)
+    rf.fit(X_train, y_train)
+    y_pred = rf.predict(X_test)
+    r2 = r2_score(y_test, y_pred)
+    return r2
+
+def evaluate_rf_classifier(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y)
+    rf = RandomForestClassifier(n_estimators=100)
+    rf.fit(X_train, y_train)
+    y_pred = rf.predict(X_test)
+    acc = accuracy_score(y_test, y_pred)
+    return acc
+
+def plot_performance(embedding_result, raw_rf_acc, title=""):
+    if isinstance(embedding_result, pd.DataFrame):
+        ench_acc = (embedding_result["Actual"] == embedding_result["Predicted"]).mean()
+    else:
+        ench_acc = float(embedding_result)
+        
+    enhanced_accuracies = [ench_acc]
+    raw_accuracies = [raw_rf_acc]
+    x_labels = "Predictions on Global Network"
+    
+    x = np.arange(len(raw_accuracies))
+    width = 0.35
+
+    fig, ax = plt.subplots(figsize=(8, 6))
+    
+    bars_raw = ax.bar(x - width/2, raw_accuracies, width, label="Raw Omics", color="blue", alpha=0.7)
+    bars_enh = ax.bar(x + width/2, enhanced_accuracies, width, label="Enriched Omics", color="orange", alpha=0.7)
+
+    ax.set_xlabel(x_labels)
+    ax.set_ylabel("Accuracy")
+    ax.set_title(title)
+    ax.legend()
+    ax.grid(True, axis="y", linestyle="--", alpha=0.7)
+    
+    ax.set_xticks([])
+    
+    for bar in bars_raw:
+        height = bar.get_height()
+        ax.text(bar.get_x() + bar.get_width()/2, height, f"{height*100:.1f}%", 
+                ha='center', va='bottom', fontsize=10, fontweight='bold')
+    for bar in bars_enh:
+        height = bar.get_height()
+        ax.text(bar.get_x() + bar.get_width()/2, height, f"{height*100:.1f}%", 
+                ha='center', va='bottom', fontsize=10, fontweight='bold')
+    
+    fig.tight_layout()
+    plt.show()
+
+
+def plot_embeddings(embeddings, node_labels=None, method="tsne"):
+    X = np.array(embeddings)
+    if method.lower() == "pca":
+        reducer = PCA(n_components=2)
+    elif method.lower() == "umap":
+        reducer = UMAP(n_components=2)
+    else:
+        perplexity = min(30, X.shape[0] - 1)
+        if perplexity < 1:
+            logger.info(f"Skipping plot: not enough samples ({X.shape[0]}) for TSNE.")
+            return
+        reducer = TSNE(n_components=2, init="pca", perplexity=perplexity)
+    
+    X_reduced = reducer.fit_transform(X)
+    
+    if node_labels is None:
+        c_values = np.zeros(X.shape[0])
+    elif hasattr(node_labels, "iloc"):
+        c_values = np.array(node_labels.iloc[:, 0], dtype=float).flatten()
+    else:
+        c_values = np.array(node_labels, dtype=float).flatten()
+    
+    sns.set_theme(style="whitegrid", context="talk")
+    fig, ax = plt.subplots(figsize=(10, 8))
+    
+    scatter = ax.scatter(
+        X_reduced[:, 0], X_reduced[:, 1],
+        c=c_values,
+        cmap="viridis",
+        s=60,
+        alpha=0.9,
+        edgecolor="k" 
+    )
+    
+    ax.invert_yaxis()
+    ax.set_xlabel("Principal Component 1")
+    ax.set_ylabel("Principal Component 2")
+    ax.set_title(f"Node Embeddings in 2D space from {embeddings.shape[1]}D")
+    
+    cbar = fig.colorbar(scatter, ax=ax)
+    cbar.set_label("Node Attribute")
+    
+    fig.tight_layout()
+    plt.show()
+
+def plot_network(adjacency_matrix, weight_threshold=0.0, show_labels=False, show_edge_weights=False):
+    """
+    Plots a network graph from an adjacency matrix with improved visualization.
+    Also adds a summary table mapping node indexes to actual gene names.
+
+    Parameters:
+        adjacency_matrix (pd.DataFrame): The adjacency matrix of the network.
+        weight_threshold (float): Minimum weight to keep an edge (default: 0.0).
+        show_labels (bool): Whether to show node labels.
+        show_edge_weights (bool): Whether to show edge weights.
+    """
+    full_G = nx.from_pandas_adjacency(adjacency_matrix)
+    total_nodes = full_G.number_of_nodes()
+    total_edges = full_G.number_of_edges()
+
+    G = full_G.copy()
+
+    if weight_threshold > 0:
+        edges_to_remove = []
+        
+        for u, v, d in G.edges(data=True):
+            weight = d.get('weight', 0)
+            if weight < weight_threshold:
+                edges_to_remove.append((u, v))
+
+        G.remove_edges_from(edges_to_remove)  
+
+    isolated_nodes = list(nx.isolates(G))
+    G.remove_nodes_from(isolated_nodes)
+
+    current_nodes = list(G.nodes())
+    current_edges = G.number_of_edges()
+
+    index_mapping = {}
+    for i, node in enumerate(current_nodes):
+        index_mapping[node] = i + 1
+
+    indexed_labels = {}
+    for node in current_nodes:
+        indexed_labels[node] = str(index_mapping[node])
+
+    degrees = {}
+    for node, degree in G.degree():
+        degrees[node] = degree
+
+    max_degree = max(degrees.values()) if degrees else 1
+    node_sizes = []
+    for node in G.nodes():
+        node_sizes.append(150 + (degrees[node] / max_degree) * 300)
+
+    edge_weights = []
+    for u, v in G.edges():
+        weight = G[u][v]['weight']
+        edge_weights.append(weight)
+
+    edge_widths = []
+    if edge_weights:
+        min_weight = min(edge_weights)
+        max_weight = max(edge_weights)
+        for w in edge_weights:
+            edge_widths.append(2 + 4 * (w - min_weight) / (max_weight - min_weight + 1e-6))
+
+    pos = nx.kamada_kawai_layout(G)
+    fig, ax_graph = plt.subplots(figsize=(14, 8))
+
+    nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color="gold", edgecolors="black", linewidths=1.5, alpha=0.9, ax=ax_graph)
+    nx.draw_networkx_edges(G, pos, alpha=0.8, width=edge_widths, edge_color="black", ax=ax_graph)
+
+    if show_edge_weights and edge_weights:
+        edge_labels = nx.get_edge_attributes(G, 'weight')
+        
+        formatted_edge_labels = {}
+        for edge, weight in edge_labels.items():
+            formatted_edge_labels[edge] = f"{weight:.4f}"
+        
+        nx.draw_networkx_edge_labels(G, pos, edge_labels=formatted_edge_labels, font_size=9, ax=ax_graph)
+
+    if show_labels:
+        nx.draw_networkx_labels(G, pos, labels=indexed_labels, font_size=11, font_color="black", ax=ax_graph)
+
+    ax_graph.set_xticks([])
+    ax_graph.set_yticks([])
+    ax_graph.set_frame_on(False)
+
+    ax_graph.set_title("Network Visualization", fontsize=16)
+    ax_graph.axis("off")
+
+    summary_text = f"""
+    Full Cluster Nodes: {total_nodes}
+    Full Cluster Edges: {total_edges}
+    Filtered Nodes: {len(current_nodes)}
+    Filtered Edges: {current_edges}
+    """
+
+    ax_graph.text(0.9, 1.05, summary_text, transform=ax_graph.transAxes, fontsize=14, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+    plt.show()
+
+    mapping_data = []
+    for node in current_nodes:
+        mapping_data.append((index_mapping[node], node, degrees[node]))
+
+    mapping_df = pd.DataFrame(mapping_data, columns=["Index", "Omic", "Degree"])
+    mapping_df = mapping_df.sort_values(by="Degree", ascending=False).set_index("Index")
+
+    return mapping_df
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def compare_clusters(louvain_clusters: list, smccnet_clusters: list, pheno: pd.DataFrame, 
+                     omics_merged: pd.DataFrame, label1: str = "Louvain", label2: str = "SmCCNet"):
+    """
+    Compare clusters from two methods by computing the correlation for each induced subnetwork.
+    Both inputs are expected to be lists of pandas DataFrames. If the lists have different lengths,
+    only the first min(n, m) clusters are compared.
+    
+    Parameters:
+        louvain_clusters: list of pd.DataFrame
+            Each DataFrame represents an induced subnetwork (from Louvain).
+        smccnet_clusters: list of pd.DataFrame
+            Each DataFrame represents an induced subnetwork (from SMCCNET).
+        pheno: pd.DataFrame
+            Phenotype data (the first column is used).
+        omics_merged: pd.DataFrame
+            Full omics data used to map SMCCNET clusters to sample-based data.
+        label1: str
+            Label for the first method.
+        label2: str
+            Label for the second method.
+    """
+    smccnet_clusters_fixed = []
+
+    for cluster_df in smccnet_clusters:
+        valid_genes = [] 
+        
+        for gene in cluster_df.index:
+            if gene in omics_merged.columns:
+                valid_genes.append(gene)
+        
+        if len(valid_genes) > 0:
+            sample_level_data = omics_merged[valid_genes]
+            smccnet_clusters_fixed.append(sample_level_data)
+
+    min_len = min(len(louvain_clusters), len(smccnet_clusters_fixed))
+    louvain_clusters = louvain_clusters[:min_len]
+    smccnet_clusters_fixed = smccnet_clusters_fixed[:min_len]
+
+    results = []
+    
+    for i, (df_louvain, df_smccnet) in enumerate(zip(louvain_clusters, smccnet_clusters_fixed), start=1):
+        size_louvain, corr_louvain = compute_cluster_correlation_from_df(df_louvain, pheno)
+        size_smccnet, corr_smccnet = compute_cluster_correlation_from_df(df_smccnet, pheno)
+
+        if corr_louvain is not None and corr_smccnet is not None:
+            results.append((f"Cluster_{i}", size_louvain, corr_louvain, size_smccnet, corr_smccnet))
+
+    df_results = pd.DataFrame(results, columns=["Cluster", "Louvain Size", "Louvain Correlation", 
+                                                "SMCCNET Size", "SMCCNET Correlation"])
+    
+    fig, ax = plt.subplots(figsize=(10, 5))
+    ax.plot(df_results.index + 1, df_results["Louvain Correlation"], marker="o", linestyle="-", 
+            label=label1, color="blue")
+    ax.plot(df_results.index + 1, df_results["SMCCNET Correlation"], marker="s", linestyle="--", 
+            label=label2, color="red")
+
+    for i, row in df_results.iterrows():
+        ax.text(i + 1, row["Louvain Correlation"] + 0.05, 
+                f"{row['Louvain Size']}", ha="center", fontsize=10, 
+                color="blue", fontweight="bold", bbox=dict(facecolor="white", alpha=0.7, edgecolor="none"))
+        
+        ax.text(i + 1, row["SMCCNET Correlation"] + 0.05, 
+                f"{row['SMCCNET Size']}", ha="center", fontsize=10, 
+                color="red", fontweight="bold", bbox=dict(facecolor="white", alpha=0.7, edgecolor="none"))
+
+    ax.set_xticks(range(1, len(df_results) + 1))
+    ax.set_xlabel("Cluster Index")
+    ax.set_ylabel("Correlation")
+    ax.set_title(f"Cluster correlation:{label1} vs {label2}")
+    ax.legend()
+    ax.grid(True)
+    fig.tight_layout(pad=3)
+    plt.show()
+
+    return df_results
diff --git a/bioneuralnet/utils/path_utils.py b/bioneuralnet/utils/path_utils.py
index acb0901..3228236 100644
--- a/bioneuralnet/utils/path_utils.py
+++ b/bioneuralnet/utils/path_utils.py
@@ -1,7 +1,6 @@
 import os
 import logging
 from importlib import resources
-from pathlib import Path
 
 logger = logging.getLogger(__name__)
 
diff --git a/bioneuralnet/utils/rdata_to_csv.R b/bioneuralnet/utils/rdata_to_csv.R
new file mode 100644
index 0000000..317d382
--- /dev/null
+++ b/bioneuralnet/utils/rdata_to_csv.R
@@ -0,0 +1,39 @@
+args <- commandArgs(trailingOnly = TRUE)
+input_file <- args[1]
+csv_file <- args[2]
+
+orig_input <- input_file
+orig_csv <- csv_file
+
+rm(list = setdiff(ls(), c("orig_input", "orig_csv")))
+
+success <- tryCatch({
+  load(orig_input)
+  loaded_objs <- ls()
+  
+  if (length(loaded_objs) == 1) {
+    mat <- get(loaded_objs[1])
+  } else if (exists("AdjacencyMatrix")) {
+    mat <- AdjacencyMatrix
+  } else if (exists("M")) {
+    mat <- M
+  } else {
+    stop("Error: Neither 'AdjacencyMatrix' nor 'M' was found in the RData file.")
+  }
+  
+    message("Attempting to write CSV to: ", orig_csv)
+    write.csv(mat, file = orig_csv, row.names = TRUE)
+    if (file.exists(orig_csv)) {
+    message("CSV file successfully written to: ", orig_csv)
+    } else {
+    message("CSV file was NOT created at: ", orig_csv)
+    }
+
+  TRUE
+}, error = function(e) {
+  message("Skipping network conversion for file: ", orig_input)
+  message("Error: ", e$message)
+  FALSE
+})
+
+quit(save = "no", status = 0)
diff --git a/bioneuralnet/utils/rdata_to_csv.py b/bioneuralnet/utils/rdata_to_csv.py
index 072a7d1..504c86f 100644
--- a/bioneuralnet/utils/rdata_to_csv.py
+++ b/bioneuralnet/utils/rdata_to_csv.py
@@ -1,15 +1,14 @@
 import subprocess
 import shutil
 from pathlib import Path
-from logger import get_logger
+from .logger import get_logger
 
-
-def convert_rdata_to_csv(rdata_file: Path, csv_file: Path) -> None:
+def rdata_to_csv_file(rdata_file: Path, csv_file: Path) -> None:
     """
-    Convert an .Rdata file to CSV by invoking the convert_Rdata_to_csv.R script using Rscript.
+    Convert an .Rdata file to CSV by invoking the rdata_to_csv.R script using Rscript.
 
     This function assumes that:
-      - The R script 'convert_Rdata_to_csv.R' is in the same directory as this module.
+      - The R script '=data_to_csv.R' is in the same directory as this module.
       - R is installed and Rscript is available in the system PATH.
       - The RData file contains an object named either "AdjacencyMatrix" or "M".
 
@@ -27,7 +26,7 @@ def convert_rdata_to_csv(rdata_file: Path, csv_file: Path) -> None:
     if rscript_path is None:
         raise EnvironmentError("Rscript not found in system PATH.")
 
-    script_path = Path(__file__).parent / "convert_Rdata_to_csv.R"
+    script_path = Path(__file__).parent / "rdata_to_csv.R"
     if not script_path.exists():
         raise FileNotFoundError(f"R script not found: {script_path}")
 
@@ -42,13 +41,4 @@ def convert_rdata_to_csv(rdata_file: Path, csv_file: Path) -> None:
         raise Exception("R script execution failed.")
     else:
         logger.info(result.stdout)
-        logger.info(f"CSV file saved to: {csv_file}")
-
-
-# Example usage:
-if __name__ == "__main__":
-    # Replace with your actual paths
-    rdata_path = Path("path/to/your_file.Rdata")
-    csv_output_path = Path("path/to/your_file.csv")
-
-    convert_rdata_to_csv(rdata_path, csv_output_path)
+        logger.info(f"CSV file saved to: {csv_file}")
\ No newline at end of file
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 4914502..5c8ef93 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -2,3 +2,5 @@ sphinx
 sphinx_rtd_theme
 sphinx-autosummary-accessors
 sphinxcontrib-napoleon
+pandoc
+nbsphinx
\ No newline at end of file
diff --git a/docs/source/TOPMED.ipynb b/docs/source/TOPMED.ipynb
new file mode 100644
index 0000000..8f1ecb0
--- /dev/null
+++ b/docs/source/TOPMED.ipynb
@@ -0,0 +1,2590 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TOPMED Presentation\n",
+    "\n",
+    "#### Demonstrates a **step-by-step** guide to using BioNeuralNet for multi-omics analysis and research."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# In terminal use:\n",
+    "pip install bioneuralnet\n",
+    "\n",
+    "# In Jupiter notebook use\n",
+    "!{sys.executable} -m pip install bioneuralnet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.datasets import DatasetLoader\n",
+    "from bioneuralnet.clustering import CorrelatedLouvain\n",
+    "from bioneuralnet.clustering import HybridLouvain\n",
+    "from bioneuralnet.network_embedding import GNNEmbedding\n",
+    "from bioneuralnet.subject_representation import GraphEmbedding\n",
+    "from bioneuralnet.downstream_task import DPMON\n",
+    "from bioneuralnet.metrics import compute_correlation, compute_cluster_correlation_from_df, convert_louvain_to_adjacency\n",
+    "from bioneuralnet.metrics import CheckVariance, network_remove_low_variance, network_remove_high_zero_fraction\n",
+    "\n",
+    "from bioneuralnet.external_tools import SmCCNet\n",
+    "from bioneuralnet.external_tools import WGCNA\n",
+    "from bioneuralnet.utils.notebook_example import plot_embeddings, plot_network, plot_performance, compare_clusters\n",
+    "from bioneuralnet.utils import get_logger"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- #### Your dataset import may look something like this.\n",
+    "- #### After loading your data, the remaining steps will be the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "genomic = pd.read_csv( \"genes.csv\")\n",
+    "proteomics = pd.read_csv(\"proteins.csv\")\n",
+    "metabolites = pd.read_csv(\"metabolites.csv\")\n",
+    "phenotype = pd.read_csv(\"phenotype.csv\")\n",
+    "clinical_data = pd.read_csv(\"clinical.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene_1</th>\n",
+       "      <th>Gene_2</th>\n",
+       "      <th>Gene_3</th>\n",
+       "      <th>Gene_4</th>\n",
+       "      <th>Gene_5</th>\n",
+       "      <th>Gene_6</th>\n",
+       "      <th>Gene_7</th>\n",
+       "      <th>Gene_8</th>\n",
+       "      <th>Gene_9</th>\n",
+       "      <th>Gene_10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Gene_491</th>\n",
+       "      <th>Gene_492</th>\n",
+       "      <th>Gene_493</th>\n",
+       "      <th>Gene_494</th>\n",
+       "      <th>Gene_495</th>\n",
+       "      <th>Gene_496</th>\n",
+       "      <th>Gene_497</th>\n",
+       "      <th>Gene_498</th>\n",
+       "      <th>Gene_499</th>\n",
+       "      <th>Gene_500</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>22.485701</td>\n",
+       "      <td>40.353720</td>\n",
+       "      <td>31.025745</td>\n",
+       "      <td>20.847206</td>\n",
+       "      <td>26.697293</td>\n",
+       "      <td>30.205449</td>\n",
+       "      <td>23.512005</td>\n",
+       "      <td>33.677622</td>\n",
+       "      <td>19.430333</td>\n",
+       "      <td>30.260153</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.967210</td>\n",
+       "      <td>13.334132</td>\n",
+       "      <td>13.262070</td>\n",
+       "      <td>13.608147</td>\n",
+       "      <td>13.338606</td>\n",
+       "      <td>15.247613</td>\n",
+       "      <td>13.603157</td>\n",
+       "      <td>14.727795</td>\n",
+       "      <td>13.400950</td>\n",
+       "      <td>12.769172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>37.058850</td>\n",
+       "      <td>34.052233</td>\n",
+       "      <td>33.487020</td>\n",
+       "      <td>23.531461</td>\n",
+       "      <td>26.754628</td>\n",
+       "      <td>31.735945</td>\n",
+       "      <td>22.795952</td>\n",
+       "      <td>29.301536</td>\n",
+       "      <td>14.936397</td>\n",
+       "      <td>30.823015</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.886247</td>\n",
+       "      <td>13.241386</td>\n",
+       "      <td>12.445773</td>\n",
+       "      <td>13.400288</td>\n",
+       "      <td>12.587993</td>\n",
+       "      <td>15.048676</td>\n",
+       "      <td>12.018609</td>\n",
+       "      <td>14.513958</td>\n",
+       "      <td>12.066379</td>\n",
+       "      <td>12.583460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>20.530767</td>\n",
+       "      <td>31.669623</td>\n",
+       "      <td>35.189567</td>\n",
+       "      <td>20.952544</td>\n",
+       "      <td>25.018826</td>\n",
+       "      <td>32.157235</td>\n",
+       "      <td>25.069464</td>\n",
+       "      <td>22.853719</td>\n",
+       "      <td>18.220225</td>\n",
+       "      <td>23.092805</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.198357</td>\n",
+       "      <td>12.869640</td>\n",
+       "      <td>12.841290</td>\n",
+       "      <td>13.127437</td>\n",
+       "      <td>12.607377</td>\n",
+       "      <td>14.398177</td>\n",
+       "      <td>12.554904</td>\n",
+       "      <td>14.339382</td>\n",
+       "      <td>12.891962</td>\n",
+       "      <td>12.760553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>33.186888</td>\n",
+       "      <td>38.480880</td>\n",
+       "      <td>18.897097</td>\n",
+       "      <td>31.823300</td>\n",
+       "      <td>34.049383</td>\n",
+       "      <td>38.799887</td>\n",
+       "      <td>24.106468</td>\n",
+       "      <td>12.397175</td>\n",
+       "      <td>13.724255</td>\n",
+       "      <td>27.703085</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.197502</td>\n",
+       "      <td>13.312536</td>\n",
+       "      <td>11.645099</td>\n",
+       "      <td>13.933684</td>\n",
+       "      <td>12.103087</td>\n",
+       "      <td>15.133432</td>\n",
+       "      <td>12.436027</td>\n",
+       "      <td>15.116888</td>\n",
+       "      <td>12.810732</td>\n",
+       "      <td>12.972879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>28.961981</td>\n",
+       "      <td>41.060494</td>\n",
+       "      <td>28.494956</td>\n",
+       "      <td>18.374495</td>\n",
+       "      <td>30.815238</td>\n",
+       "      <td>24.004535</td>\n",
+       "      <td>29.616296</td>\n",
+       "      <td>24.364045</td>\n",
+       "      <td>11.409338</td>\n",
+       "      <td>33.599828</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.157141</td>\n",
+       "      <td>13.386978</td>\n",
+       "      <td>13.007109</td>\n",
+       "      <td>13.826532</td>\n",
+       "      <td>12.343930</td>\n",
+       "      <td>15.280175</td>\n",
+       "      <td>13.363962</td>\n",
+       "      <td>14.008675</td>\n",
+       "      <td>12.479124</td>\n",
+       "      <td>12.156407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>24.520652</td>\n",
+       "      <td>28.595409</td>\n",
+       "      <td>31.299666</td>\n",
+       "      <td>32.095379</td>\n",
+       "      <td>33.659730</td>\n",
+       "      <td>29.353948</td>\n",
+       "      <td>29.871659</td>\n",
+       "      <td>25.089732</td>\n",
+       "      <td>14.437588</td>\n",
+       "      <td>27.261916</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.748228</td>\n",
+       "      <td>13.096453</td>\n",
+       "      <td>12.768936</td>\n",
+       "      <td>13.228560</td>\n",
+       "      <td>11.595884</td>\n",
+       "      <td>15.251601</td>\n",
+       "      <td>12.055375</td>\n",
+       "      <td>15.530606</td>\n",
+       "      <td>13.644383</td>\n",
+       "      <td>13.018032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>31.252789</td>\n",
+       "      <td>28.988087</td>\n",
+       "      <td>29.574195</td>\n",
+       "      <td>31.189288</td>\n",
+       "      <td>32.098841</td>\n",
+       "      <td>26.849138</td>\n",
+       "      <td>22.619669</td>\n",
+       "      <td>28.006670</td>\n",
+       "      <td>8.615549</td>\n",
+       "      <td>30.838843</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.460876</td>\n",
+       "      <td>12.994504</td>\n",
+       "      <td>13.325608</td>\n",
+       "      <td>13.386970</td>\n",
+       "      <td>12.335143</td>\n",
+       "      <td>14.966097</td>\n",
+       "      <td>13.998975</td>\n",
+       "      <td>13.250821</td>\n",
+       "      <td>12.947672</td>\n",
+       "      <td>13.161434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>24.894826</td>\n",
+       "      <td>25.944887</td>\n",
+       "      <td>30.852641</td>\n",
+       "      <td>26.705158</td>\n",
+       "      <td>30.102546</td>\n",
+       "      <td>37.197952</td>\n",
+       "      <td>35.032309</td>\n",
+       "      <td>34.775576</td>\n",
+       "      <td>13.354611</td>\n",
+       "      <td>29.697348</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.884821</td>\n",
+       "      <td>12.785372</td>\n",
+       "      <td>12.954534</td>\n",
+       "      <td>13.906209</td>\n",
+       "      <td>12.074805</td>\n",
+       "      <td>15.519179</td>\n",
+       "      <td>12.742078</td>\n",
+       "      <td>14.445011</td>\n",
+       "      <td>12.129990</td>\n",
+       "      <td>13.844271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>17.034337</td>\n",
+       "      <td>38.574705</td>\n",
+       "      <td>25.095201</td>\n",
+       "      <td>37.062442</td>\n",
+       "      <td>35.417758</td>\n",
+       "      <td>29.753145</td>\n",
+       "      <td>25.855997</td>\n",
+       "      <td>25.603043</td>\n",
+       "      <td>13.180794</td>\n",
+       "      <td>31.188598</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.278454</td>\n",
+       "      <td>13.445404</td>\n",
+       "      <td>12.457436</td>\n",
+       "      <td>14.036252</td>\n",
+       "      <td>13.412114</td>\n",
+       "      <td>14.158416</td>\n",
+       "      <td>13.284701</td>\n",
+       "      <td>13.662274</td>\n",
+       "      <td>12.943670</td>\n",
+       "      <td>13.996352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>20.839167</td>\n",
+       "      <td>27.099788</td>\n",
+       "      <td>31.038453</td>\n",
+       "      <td>19.410859</td>\n",
+       "      <td>31.818995</td>\n",
+       "      <td>33.493625</td>\n",
+       "      <td>22.769369</td>\n",
+       "      <td>22.869481</td>\n",
+       "      <td>16.839248</td>\n",
+       "      <td>24.644476</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.519057</td>\n",
+       "      <td>13.308512</td>\n",
+       "      <td>12.261076</td>\n",
+       "      <td>11.211687</td>\n",
+       "      <td>12.058898</td>\n",
+       "      <td>15.349873</td>\n",
+       "      <td>13.166209</td>\n",
+       "      <td>15.295902</td>\n",
+       "      <td>13.257230</td>\n",
+       "      <td>13.178058</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 500 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Gene_1     Gene_2     Gene_3     Gene_4     Gene_5     Gene_6  \\\n",
+       "Samp_1    22.485701  40.353720  31.025745  20.847206  26.697293  30.205449   \n",
+       "Samp_2    37.058850  34.052233  33.487020  23.531461  26.754628  31.735945   \n",
+       "Samp_3    20.530767  31.669623  35.189567  20.952544  25.018826  32.157235   \n",
+       "Samp_4    33.186888  38.480880  18.897097  31.823300  34.049383  38.799887   \n",
+       "Samp_5    28.961981  41.060494  28.494956  18.374495  30.815238  24.004535   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  24.520652  28.595409  31.299666  32.095379  33.659730  29.353948   \n",
+       "Samp_355  31.252789  28.988087  29.574195  31.189288  32.098841  26.849138   \n",
+       "Samp_356  24.894826  25.944887  30.852641  26.705158  30.102546  37.197952   \n",
+       "Samp_357  17.034337  38.574705  25.095201  37.062442  35.417758  29.753145   \n",
+       "Samp_358  20.839167  27.099788  31.038453  19.410859  31.818995  33.493625   \n",
+       "\n",
+       "             Gene_7     Gene_8     Gene_9    Gene_10  ...   Gene_491  \\\n",
+       "Samp_1    23.512005  33.677622  19.430333  30.260153  ...  12.967210   \n",
+       "Samp_2    22.795952  29.301536  14.936397  30.823015  ...  11.886247   \n",
+       "Samp_3    25.069464  22.853719  18.220225  23.092805  ...  12.198357   \n",
+       "Samp_4    24.106468  12.397175  13.724255  27.703085  ...  12.197502   \n",
+       "Samp_5    29.616296  24.364045  11.409338  33.599828  ...  14.157141   \n",
+       "...             ...        ...        ...        ...  ...        ...   \n",
+       "Samp_354  29.871659  25.089732  14.437588  27.261916  ...  11.748228   \n",
+       "Samp_355  22.619669  28.006670   8.615549  30.838843  ...  11.460876   \n",
+       "Samp_356  35.032309  34.775576  13.354611  29.697348  ...  12.884821   \n",
+       "Samp_357  25.855997  25.603043  13.180794  31.188598  ...  13.278454   \n",
+       "Samp_358  22.769369  22.869481  16.839248  24.644476  ...  12.519057   \n",
+       "\n",
+       "           Gene_492   Gene_493   Gene_494   Gene_495   Gene_496   Gene_497  \\\n",
+       "Samp_1    13.334132  13.262070  13.608147  13.338606  15.247613  13.603157   \n",
+       "Samp_2    13.241386  12.445773  13.400288  12.587993  15.048676  12.018609   \n",
+       "Samp_3    12.869640  12.841290  13.127437  12.607377  14.398177  12.554904   \n",
+       "Samp_4    13.312536  11.645099  13.933684  12.103087  15.133432  12.436027   \n",
+       "Samp_5    13.386978  13.007109  13.826532  12.343930  15.280175  13.363962   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  13.096453  12.768936  13.228560  11.595884  15.251601  12.055375   \n",
+       "Samp_355  12.994504  13.325608  13.386970  12.335143  14.966097  13.998975   \n",
+       "Samp_356  12.785372  12.954534  13.906209  12.074805  15.519179  12.742078   \n",
+       "Samp_357  13.445404  12.457436  14.036252  13.412114  14.158416  13.284701   \n",
+       "Samp_358  13.308512  12.261076  11.211687  12.058898  15.349873  13.166209   \n",
+       "\n",
+       "           Gene_498   Gene_499   Gene_500  \n",
+       "Samp_1    14.727795  13.400950  12.769172  \n",
+       "Samp_2    14.513958  12.066379  12.583460  \n",
+       "Samp_3    14.339382  12.891962  12.760553  \n",
+       "Samp_4    15.116888  12.810732  12.972879  \n",
+       "Samp_5    14.008675  12.479124  12.156407  \n",
+       "...             ...        ...        ...  \n",
+       "Samp_354  15.530606  13.644383  13.018032  \n",
+       "Samp_355  13.250821  12.947672  13.161434  \n",
+       "Samp_356  14.445011  12.129990  13.844271  \n",
+       "Samp_357  13.662274  12.943670  13.996352  \n",
+       "Samp_358  15.295902  13.257230  13.178058  \n",
+       "\n",
+       "[358 rows x 500 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mir_1</th>\n",
+       "      <th>Mir_2</th>\n",
+       "      <th>Mir_3</th>\n",
+       "      <th>Mir_4</th>\n",
+       "      <th>Mir_5</th>\n",
+       "      <th>Mir_6</th>\n",
+       "      <th>Mir_7</th>\n",
+       "      <th>Mir_8</th>\n",
+       "      <th>Mir_9</th>\n",
+       "      <th>Mir_10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Mir_91</th>\n",
+       "      <th>Mir_92</th>\n",
+       "      <th>Mir_93</th>\n",
+       "      <th>Mir_94</th>\n",
+       "      <th>Mir_95</th>\n",
+       "      <th>Mir_96</th>\n",
+       "      <th>Mir_97</th>\n",
+       "      <th>Mir_98</th>\n",
+       "      <th>Mir_99</th>\n",
+       "      <th>Mir_100</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>15.223913</td>\n",
+       "      <td>17.545826</td>\n",
+       "      <td>15.784719</td>\n",
+       "      <td>14.891983</td>\n",
+       "      <td>10.348205</td>\n",
+       "      <td>9.689755</td>\n",
+       "      <td>11.093630</td>\n",
+       "      <td>13.189246</td>\n",
+       "      <td>12.526561</td>\n",
+       "      <td>10.311609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.551462</td>\n",
+       "      <td>5.407878</td>\n",
+       "      <td>13.933045</td>\n",
+       "      <td>10.287521</td>\n",
+       "      <td>10.213316</td>\n",
+       "      <td>10.609973</td>\n",
+       "      <td>14.554996</td>\n",
+       "      <td>10.955650</td>\n",
+       "      <td>11.422531</td>\n",
+       "      <td>10.862970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>16.306965</td>\n",
+       "      <td>16.672830</td>\n",
+       "      <td>13.361529</td>\n",
+       "      <td>14.488549</td>\n",
+       "      <td>12.660905</td>\n",
+       "      <td>11.333613</td>\n",
+       "      <td>11.254930</td>\n",
+       "      <td>13.457435</td>\n",
+       "      <td>13.279747</td>\n",
+       "      <td>10.428848</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.862413</td>\n",
+       "      <td>7.309226</td>\n",
+       "      <td>13.586180</td>\n",
+       "      <td>11.975544</td>\n",
+       "      <td>11.496937</td>\n",
+       "      <td>10.653742</td>\n",
+       "      <td>14.414225</td>\n",
+       "      <td>11.811004</td>\n",
+       "      <td>12.413667</td>\n",
+       "      <td>10.719110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>16.545119</td>\n",
+       "      <td>16.735005</td>\n",
+       "      <td>14.617472</td>\n",
+       "      <td>17.845267</td>\n",
+       "      <td>13.822790</td>\n",
+       "      <td>11.329333</td>\n",
+       "      <td>11.171749</td>\n",
+       "      <td>12.715107</td>\n",
+       "      <td>13.787771</td>\n",
+       "      <td>10.364577</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.874958</td>\n",
+       "      <td>7.754733</td>\n",
+       "      <td>13.847029</td>\n",
+       "      <td>12.424403</td>\n",
+       "      <td>10.930177</td>\n",
+       "      <td>10.255484</td>\n",
+       "      <td>13.570352</td>\n",
+       "      <td>11.311925</td>\n",
+       "      <td>11.072915</td>\n",
+       "      <td>11.418794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>13.986899</td>\n",
+       "      <td>16.207432</td>\n",
+       "      <td>16.293078</td>\n",
+       "      <td>17.725286</td>\n",
+       "      <td>12.300565</td>\n",
+       "      <td>9.844108</td>\n",
+       "      <td>11.586551</td>\n",
+       "      <td>11.878702</td>\n",
+       "      <td>14.594392</td>\n",
+       "      <td>10.936651</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.615623</td>\n",
+       "      <td>6.693593</td>\n",
+       "      <td>13.840728</td>\n",
+       "      <td>12.245466</td>\n",
+       "      <td>10.836894</td>\n",
+       "      <td>11.502232</td>\n",
+       "      <td>15.483399</td>\n",
+       "      <td>10.812811</td>\n",
+       "      <td>10.121957</td>\n",
+       "      <td>11.039089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>16.338332</td>\n",
+       "      <td>17.393869</td>\n",
+       "      <td>16.397925</td>\n",
+       "      <td>15.853725</td>\n",
+       "      <td>13.387675</td>\n",
+       "      <td>10.599414</td>\n",
+       "      <td>12.355466</td>\n",
+       "      <td>12.450426</td>\n",
+       "      <td>13.778779</td>\n",
+       "      <td>10.405327</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.146865</td>\n",
+       "      <td>8.104206</td>\n",
+       "      <td>13.903452</td>\n",
+       "      <td>11.423350</td>\n",
+       "      <td>9.997107</td>\n",
+       "      <td>10.744586</td>\n",
+       "      <td>14.465583</td>\n",
+       "      <td>11.730166</td>\n",
+       "      <td>12.206151</td>\n",
+       "      <td>10.724849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>15.065065</td>\n",
+       "      <td>16.079830</td>\n",
+       "      <td>14.635616</td>\n",
+       "      <td>17.013845</td>\n",
+       "      <td>11.612843</td>\n",
+       "      <td>9.850100</td>\n",
+       "      <td>11.375670</td>\n",
+       "      <td>12.870558</td>\n",
+       "      <td>13.873811</td>\n",
+       "      <td>11.369083</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.110691</td>\n",
+       "      <td>8.124900</td>\n",
+       "      <td>14.316491</td>\n",
+       "      <td>12.599296</td>\n",
+       "      <td>10.302683</td>\n",
+       "      <td>11.286541</td>\n",
+       "      <td>14.478041</td>\n",
+       "      <td>11.141163</td>\n",
+       "      <td>11.102427</td>\n",
+       "      <td>11.993050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>15.997576</td>\n",
+       "      <td>15.448951</td>\n",
+       "      <td>15.355566</td>\n",
+       "      <td>16.501752</td>\n",
+       "      <td>11.701778</td>\n",
+       "      <td>11.323521</td>\n",
+       "      <td>11.610163</td>\n",
+       "      <td>12.733208</td>\n",
+       "      <td>13.650662</td>\n",
+       "      <td>10.770903</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.711232</td>\n",
+       "      <td>6.977555</td>\n",
+       "      <td>14.805837</td>\n",
+       "      <td>12.283549</td>\n",
+       "      <td>10.847157</td>\n",
+       "      <td>9.833187</td>\n",
+       "      <td>14.037476</td>\n",
+       "      <td>11.082880</td>\n",
+       "      <td>11.708466</td>\n",
+       "      <td>10.654141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>15.206862</td>\n",
+       "      <td>14.395378</td>\n",
+       "      <td>16.218001</td>\n",
+       "      <td>16.044955</td>\n",
+       "      <td>13.650741</td>\n",
+       "      <td>11.057462</td>\n",
+       "      <td>13.324846</td>\n",
+       "      <td>13.070428</td>\n",
+       "      <td>16.109746</td>\n",
+       "      <td>11.253359</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.497926</td>\n",
+       "      <td>6.840175</td>\n",
+       "      <td>15.048896</td>\n",
+       "      <td>12.615673</td>\n",
+       "      <td>10.583973</td>\n",
+       "      <td>12.708186</td>\n",
+       "      <td>16.325254</td>\n",
+       "      <td>10.789635</td>\n",
+       "      <td>10.830833</td>\n",
+       "      <td>10.983455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>14.474129</td>\n",
+       "      <td>15.482863</td>\n",
+       "      <td>15.512549</td>\n",
+       "      <td>15.136613</td>\n",
+       "      <td>14.531277</td>\n",
+       "      <td>10.713539</td>\n",
+       "      <td>11.632242</td>\n",
+       "      <td>13.792981</td>\n",
+       "      <td>13.486404</td>\n",
+       "      <td>10.870028</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.902283</td>\n",
+       "      <td>7.339226</td>\n",
+       "      <td>14.295151</td>\n",
+       "      <td>11.853855</td>\n",
+       "      <td>10.439473</td>\n",
+       "      <td>11.897782</td>\n",
+       "      <td>16.684790</td>\n",
+       "      <td>10.847185</td>\n",
+       "      <td>11.491449</td>\n",
+       "      <td>11.684467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>15.094188</td>\n",
+       "      <td>16.047304</td>\n",
+       "      <td>15.298871</td>\n",
+       "      <td>17.022665</td>\n",
+       "      <td>15.046043</td>\n",
+       "      <td>10.931386</td>\n",
+       "      <td>12.289466</td>\n",
+       "      <td>14.327210</td>\n",
+       "      <td>13.533348</td>\n",
+       "      <td>9.792684</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.017328</td>\n",
+       "      <td>7.814296</td>\n",
+       "      <td>14.267007</td>\n",
+       "      <td>12.117150</td>\n",
+       "      <td>11.832061</td>\n",
+       "      <td>11.021241</td>\n",
+       "      <td>14.533734</td>\n",
+       "      <td>12.015799</td>\n",
+       "      <td>11.551237</td>\n",
+       "      <td>11.221372</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 100 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Mir_1      Mir_2      Mir_3      Mir_4      Mir_5      Mir_6  \\\n",
+       "Samp_1    15.223913  17.545826  15.784719  14.891983  10.348205   9.689755   \n",
+       "Samp_2    16.306965  16.672830  13.361529  14.488549  12.660905  11.333613   \n",
+       "Samp_3    16.545119  16.735005  14.617472  17.845267  13.822790  11.329333   \n",
+       "Samp_4    13.986899  16.207432  16.293078  17.725286  12.300565   9.844108   \n",
+       "Samp_5    16.338332  17.393869  16.397925  15.853725  13.387675  10.599414   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  15.065065  16.079830  14.635616  17.013845  11.612843   9.850100   \n",
+       "Samp_355  15.997576  15.448951  15.355566  16.501752  11.701778  11.323521   \n",
+       "Samp_356  15.206862  14.395378  16.218001  16.044955  13.650741  11.057462   \n",
+       "Samp_357  14.474129  15.482863  15.512549  15.136613  14.531277  10.713539   \n",
+       "Samp_358  15.094188  16.047304  15.298871  17.022665  15.046043  10.931386   \n",
+       "\n",
+       "              Mir_7      Mir_8      Mir_9     Mir_10  ...    Mir_91    Mir_92  \\\n",
+       "Samp_1    11.093630  13.189246  12.526561  10.311609  ...  7.551462  5.407878   \n",
+       "Samp_2    11.254930  13.457435  13.279747  10.428848  ...  6.862413  7.309226   \n",
+       "Samp_3    11.171749  12.715107  13.787771  10.364577  ...  6.874958  7.754733   \n",
+       "Samp_4    11.586551  11.878702  14.594392  10.936651  ...  9.615623  6.693593   \n",
+       "Samp_5    12.355466  12.450426  13.778779  10.405327  ...  9.146865  8.104206   \n",
+       "...             ...        ...        ...        ...  ...       ...       ...   \n",
+       "Samp_354  11.375670  12.870558  13.873811  11.369083  ...  8.110691  8.124900   \n",
+       "Samp_355  11.610163  12.733208  13.650662  10.770903  ...  8.711232  6.977555   \n",
+       "Samp_356  13.324846  13.070428  16.109746  11.253359  ...  7.497926  6.840175   \n",
+       "Samp_357  11.632242  13.792981  13.486404  10.870028  ...  7.902283  7.339226   \n",
+       "Samp_358  12.289466  14.327210  13.533348   9.792684  ...  8.017328  7.814296   \n",
+       "\n",
+       "             Mir_93     Mir_94     Mir_95     Mir_96     Mir_97     Mir_98  \\\n",
+       "Samp_1    13.933045  10.287521  10.213316  10.609973  14.554996  10.955650   \n",
+       "Samp_2    13.586180  11.975544  11.496937  10.653742  14.414225  11.811004   \n",
+       "Samp_3    13.847029  12.424403  10.930177  10.255484  13.570352  11.311925   \n",
+       "Samp_4    13.840728  12.245466  10.836894  11.502232  15.483399  10.812811   \n",
+       "Samp_5    13.903452  11.423350   9.997107  10.744586  14.465583  11.730166   \n",
+       "...             ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354  14.316491  12.599296  10.302683  11.286541  14.478041  11.141163   \n",
+       "Samp_355  14.805837  12.283549  10.847157   9.833187  14.037476  11.082880   \n",
+       "Samp_356  15.048896  12.615673  10.583973  12.708186  16.325254  10.789635   \n",
+       "Samp_357  14.295151  11.853855  10.439473  11.897782  16.684790  10.847185   \n",
+       "Samp_358  14.267007  12.117150  11.832061  11.021241  14.533734  12.015799   \n",
+       "\n",
+       "             Mir_99    Mir_100  \n",
+       "Samp_1    11.422531  10.862970  \n",
+       "Samp_2    12.413667  10.719110  \n",
+       "Samp_3    11.072915  11.418794  \n",
+       "Samp_4    10.121957  11.039089  \n",
+       "Samp_5    12.206151  10.724849  \n",
+       "...             ...        ...  \n",
+       "Samp_354  11.102427  11.993050  \n",
+       "Samp_355  11.708466  10.654141  \n",
+       "Samp_356  10.830833  10.983455  \n",
+       "Samp_357  11.491449  11.684467  \n",
+       "Samp_358  11.551237  11.221372  \n",
+       "\n",
+       "[358 rows x 100 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>phenotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>235.067423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>253.544991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>234.204994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>281.035429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>245.447781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>236.120451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>222.572359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>268.472285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>235.808167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>213.886123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           phenotype\n",
+       "Samp_1    235.067423\n",
+       "Samp_2    253.544991\n",
+       "Samp_3    234.204994\n",
+       "Samp_4    281.035429\n",
+       "Samp_5    245.447781\n",
+       "...              ...\n",
+       "Samp_354  236.120451\n",
+       "Samp_355  222.572359\n",
+       "Samp_356  268.472285\n",
+       "Samp_357  235.808167\n",
+       "Samp_358  213.886123\n",
+       "\n",
+       "[358 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>Chronic_Bronchitis</th>\n",
+       "      <th>Emphysema</th>\n",
+       "      <th>Asthma</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PatientID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>78</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>68</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>71</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>62</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>61</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37.6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31.3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Age  Gender   BMI  Chronic_Bronchitis  Emphysema  Asthma\n",
+       "PatientID                                                          \n",
+       "Samp_1      78       0  31.2                   1          1       0\n",
+       "Samp_2      68       1  19.2                   1          0       0\n",
+       "Samp_3      54       1  19.3                   0          1       1\n",
+       "Samp_4      47       1  36.2                   0          0       1\n",
+       "Samp_5      60       1  26.2                   0          1       1\n",
+       "...        ...     ...   ...                 ...        ...     ...\n",
+       "Samp_354    71       0  23.0                   1          0       1\n",
+       "Samp_355    62       1  25.5                   0          1       1\n",
+       "Samp_356    61       0  21.1                   1          0       0\n",
+       "Samp_357    64       0  37.6                   0          0       1\n",
+       "Samp_358    61       1  31.3                   1          0       0\n",
+       "\n",
+       "[358 rows x 6 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.datasets import DatasetLoader\n",
+    "loader = DatasetLoader(\"example1\")\n",
+    "omics1, omics2, phenotype, clinical = loader.load_data()\n",
+    "\n",
+    "display(omics1)\n",
+    "display(omics2)\n",
+    "display(phenotype)\n",
+    "display(clinical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.external_tools import SmCCNet\n",
+    "merged_omics = pd.concat([omics1, omics2, clinical, phenotype], axis=1)\n",
+    "\n",
+    "smccnet = SmCCNet(\n",
+    "    phenotype_df=phenotype,\n",
+    "    omics_dfs=[omics1, omics2],\n",
+    "    data_types=[\"genes\",\"proteins\"],\n",
+    ")\n",
+    "global_network, smccnet_clusters = smccnet.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(600, 600)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(global_network.shape)\n",
+    "display(len(smccnet_clusters))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from bioneuralnet.network_embedding import GNNEmbedding\n",
+    "\n",
+    "merged_omics = pd.concat([omics1, omics2], axis=1)\n",
+    "\n",
+    "embeddings = GNNEmbedding(\n",
+    "    adjacency_matrix=global_network,\n",
+    "    omics_data=merged_omics,\n",
+    "    phenotype_data=phenotype,\n",
+    "    clinical_data=clinical,\n",
+    "    tune=True,\n",
+    ")\n",
+    "embeddings.fit()\n",
+    "embeddings_output = embeddings.embed(as_df=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(600, 64)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(embeddings_output.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.utils import plot_embeddings\n",
+    "\n",
+    "# Using our embeddings instance we get the necessary labels for the graph.\n",
+    "global_node_labels = embeddings._prepare_node_labels()\n",
+    "embeddings_array = embeddings_output.values  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1 = plot_embeddings(embeddings_array, global_node_labels.to_frame(name=\"phenotype\"), method=\"tsne\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.subject_representation import GraphEmbedding\n",
+    "\n",
+    "enhanced_omics = GraphEmbedding(\n",
+    "    omics_data=merged_omics,\n",
+    "    embeddings=embeddings_output,\n",
+    "    phenotype_data=phenotype,\n",
+    "    tune=True,\n",
+    ")\n",
+    "enhanced_omics_df = enhanced_omics.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene_45</th>\n",
+       "      <th>Gene_197</th>\n",
+       "      <th>Gene_493</th>\n",
+       "      <th>Gene_432</th>\n",
+       "      <th>Gene_357</th>\n",
+       "      <th>Gene_382</th>\n",
+       "      <th>Gene_152</th>\n",
+       "      <th>Gene_427</th>\n",
+       "      <th>Gene_41</th>\n",
+       "      <th>Gene_130</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Gene_202</th>\n",
+       "      <th>Gene_249</th>\n",
+       "      <th>Gene_280</th>\n",
+       "      <th>Gene_62</th>\n",
+       "      <th>Mir_21</th>\n",
+       "      <th>Gene_495</th>\n",
+       "      <th>Mir_59</th>\n",
+       "      <th>Gene_340</th>\n",
+       "      <th>Gene_291</th>\n",
+       "      <th>Gene_81</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>-2.917084</td>\n",
+       "      <td>-13.297334</td>\n",
+       "      <td>-30.571794</td>\n",
+       "      <td>-29.143930</td>\n",
+       "      <td>42.021213</td>\n",
+       "      <td>-12.279085</td>\n",
+       "      <td>-6.925227</td>\n",
+       "      <td>-2.520610</td>\n",
+       "      <td>10.380707</td>\n",
+       "      <td>-6.079539</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.465338</td>\n",
+       "      <td>1.922512</td>\n",
+       "      <td>5.955446</td>\n",
+       "      <td>-2.626381</td>\n",
+       "      <td>13.947213</td>\n",
+       "      <td>-2.806483</td>\n",
+       "      <td>-0.553959</td>\n",
+       "      <td>1.726373</td>\n",
+       "      <td>-2.651651</td>\n",
+       "      <td>-2.406555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>-3.102466</td>\n",
+       "      <td>-12.954265</td>\n",
+       "      <td>-28.690063</td>\n",
+       "      <td>-27.614380</td>\n",
+       "      <td>41.053950</td>\n",
+       "      <td>-12.463960</td>\n",
+       "      <td>-6.747382</td>\n",
+       "      <td>-2.712280</td>\n",
+       "      <td>10.841243</td>\n",
+       "      <td>-6.340821</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.551755</td>\n",
+       "      <td>1.800501</td>\n",
+       "      <td>5.912429</td>\n",
+       "      <td>-2.548239</td>\n",
+       "      <td>12.697251</td>\n",
+       "      <td>-2.648551</td>\n",
+       "      <td>-0.624136</td>\n",
+       "      <td>1.708398</td>\n",
+       "      <td>-2.596500</td>\n",
+       "      <td>-2.342931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>-2.837912</td>\n",
+       "      <td>-13.429165</td>\n",
+       "      <td>-29.601810</td>\n",
+       "      <td>-28.448539</td>\n",
+       "      <td>39.378358</td>\n",
+       "      <td>-12.837721</td>\n",
+       "      <td>-6.436741</td>\n",
+       "      <td>-2.377240</td>\n",
+       "      <td>10.565243</td>\n",
+       "      <td>-6.044507</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.307515</td>\n",
+       "      <td>1.991552</td>\n",
+       "      <td>6.038545</td>\n",
+       "      <td>-2.479833</td>\n",
+       "      <td>13.769784</td>\n",
+       "      <td>-2.652630</td>\n",
+       "      <td>-0.592947</td>\n",
+       "      <td>1.583839</td>\n",
+       "      <td>-2.653498</td>\n",
+       "      <td>-2.446277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>-2.859205</td>\n",
+       "      <td>-12.821326</td>\n",
+       "      <td>-26.844345</td>\n",
+       "      <td>-29.029980</td>\n",
+       "      <td>36.148898</td>\n",
+       "      <td>-12.449477</td>\n",
+       "      <td>-7.229847</td>\n",
+       "      <td>-2.609629</td>\n",
+       "      <td>11.160280</td>\n",
+       "      <td>-6.466545</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.915145</td>\n",
+       "      <td>2.137159</td>\n",
+       "      <td>5.804663</td>\n",
+       "      <td>-2.629407</td>\n",
+       "      <td>13.458064</td>\n",
+       "      <td>-2.546526</td>\n",
+       "      <td>-0.499689</td>\n",
+       "      <td>1.648553</td>\n",
+       "      <td>-2.520416</td>\n",
+       "      <td>-2.530145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>-2.982405</td>\n",
+       "      <td>-12.612553</td>\n",
+       "      <td>-29.984057</td>\n",
+       "      <td>-29.110915</td>\n",
+       "      <td>46.903902</td>\n",
+       "      <td>-12.758489</td>\n",
+       "      <td>-6.868394</td>\n",
+       "      <td>-2.698423</td>\n",
+       "      <td>10.211682</td>\n",
+       "      <td>-6.265595</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.592827</td>\n",
+       "      <td>2.231166</td>\n",
+       "      <td>5.314841</td>\n",
+       "      <td>-2.462155</td>\n",
+       "      <td>12.673634</td>\n",
+       "      <td>-2.597200</td>\n",
+       "      <td>-0.638230</td>\n",
+       "      <td>1.709379</td>\n",
+       "      <td>-2.481453</td>\n",
+       "      <td>-2.397491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>-2.935778</td>\n",
+       "      <td>-13.323266</td>\n",
+       "      <td>-29.435019</td>\n",
+       "      <td>-27.890044</td>\n",
+       "      <td>43.240876</td>\n",
+       "      <td>-12.561521</td>\n",
+       "      <td>-6.723627</td>\n",
+       "      <td>-2.290536</td>\n",
+       "      <td>10.847353</td>\n",
+       "      <td>-6.341079</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.186933</td>\n",
+       "      <td>2.077744</td>\n",
+       "      <td>5.888022</td>\n",
+       "      <td>-2.566317</td>\n",
+       "      <td>13.658852</td>\n",
+       "      <td>-2.439809</td>\n",
+       "      <td>-0.526501</td>\n",
+       "      <td>1.692860</td>\n",
+       "      <td>-2.711183</td>\n",
+       "      <td>-2.466377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>-3.065084</td>\n",
+       "      <td>-13.295135</td>\n",
+       "      <td>-30.718263</td>\n",
+       "      <td>-29.112126</td>\n",
+       "      <td>44.613763</td>\n",
+       "      <td>-12.331960</td>\n",
+       "      <td>-6.955723</td>\n",
+       "      <td>-2.971791</td>\n",
+       "      <td>10.294231</td>\n",
+       "      <td>-6.077254</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.624207</td>\n",
+       "      <td>2.077401</td>\n",
+       "      <td>5.623264</td>\n",
+       "      <td>-2.663072</td>\n",
+       "      <td>14.311062</td>\n",
+       "      <td>-2.595351</td>\n",
+       "      <td>-0.584500</td>\n",
+       "      <td>1.673485</td>\n",
+       "      <td>-2.578159</td>\n",
+       "      <td>-2.418925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>-3.069497</td>\n",
+       "      <td>-14.104972</td>\n",
+       "      <td>-29.862862</td>\n",
+       "      <td>-30.838439</td>\n",
+       "      <td>39.916304</td>\n",
+       "      <td>-12.129115</td>\n",
+       "      <td>-6.739620</td>\n",
+       "      <td>-2.504380</td>\n",
+       "      <td>10.865619</td>\n",
+       "      <td>-6.296479</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.497804</td>\n",
+       "      <td>1.984929</td>\n",
+       "      <td>5.467676</td>\n",
+       "      <td>-2.442813</td>\n",
+       "      <td>14.854524</td>\n",
+       "      <td>-2.540575</td>\n",
+       "      <td>-0.527353</td>\n",
+       "      <td>1.648124</td>\n",
+       "      <td>-2.674988</td>\n",
+       "      <td>-2.482001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>-3.112670</td>\n",
+       "      <td>-14.041763</td>\n",
+       "      <td>-28.716949</td>\n",
+       "      <td>-28.512552</td>\n",
+       "      <td>48.270441</td>\n",
+       "      <td>-12.605819</td>\n",
+       "      <td>-6.424579</td>\n",
+       "      <td>-2.768089</td>\n",
+       "      <td>11.026826</td>\n",
+       "      <td>-6.542514</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.566136</td>\n",
+       "      <td>2.141326</td>\n",
+       "      <td>6.178911</td>\n",
+       "      <td>-2.667312</td>\n",
+       "      <td>13.193234</td>\n",
+       "      <td>-2.821949</td>\n",
+       "      <td>-0.585463</td>\n",
+       "      <td>1.659515</td>\n",
+       "      <td>-2.625582</td>\n",
+       "      <td>-2.425626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>-2.983813</td>\n",
+       "      <td>-13.657581</td>\n",
+       "      <td>-28.264297</td>\n",
+       "      <td>-28.283759</td>\n",
+       "      <td>41.077029</td>\n",
+       "      <td>-12.508963</td>\n",
+       "      <td>-6.906523</td>\n",
+       "      <td>-2.443945</td>\n",
+       "      <td>11.551972</td>\n",
+       "      <td>-6.444730</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.345094</td>\n",
+       "      <td>2.132126</td>\n",
+       "      <td>5.578996</td>\n",
+       "      <td>-2.523854</td>\n",
+       "      <td>14.942860</td>\n",
+       "      <td>-2.537228</td>\n",
+       "      <td>-0.534264</td>\n",
+       "      <td>1.689256</td>\n",
+       "      <td>-2.751487</td>\n",
+       "      <td>-2.255327</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 600 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Gene_45   Gene_197   Gene_493   Gene_432   Gene_357   Gene_382  \\\n",
+       "Samp_1   -2.917084 -13.297334 -30.571794 -29.143930  42.021213 -12.279085   \n",
+       "Samp_2   -3.102466 -12.954265 -28.690063 -27.614380  41.053950 -12.463960   \n",
+       "Samp_3   -2.837912 -13.429165 -29.601810 -28.448539  39.378358 -12.837721   \n",
+       "Samp_4   -2.859205 -12.821326 -26.844345 -29.029980  36.148898 -12.449477   \n",
+       "Samp_5   -2.982405 -12.612553 -29.984057 -29.110915  46.903902 -12.758489   \n",
+       "...            ...        ...        ...        ...        ...        ...   \n",
+       "Samp_354 -2.935778 -13.323266 -29.435019 -27.890044  43.240876 -12.561521   \n",
+       "Samp_355 -3.065084 -13.295135 -30.718263 -29.112126  44.613763 -12.331960   \n",
+       "Samp_356 -3.069497 -14.104972 -29.862862 -30.838439  39.916304 -12.129115   \n",
+       "Samp_357 -3.112670 -14.041763 -28.716949 -28.512552  48.270441 -12.605819   \n",
+       "Samp_358 -2.983813 -13.657581 -28.264297 -28.283759  41.077029 -12.508963   \n",
+       "\n",
+       "          Gene_152  Gene_427    Gene_41  Gene_130  ...   Gene_202  Gene_249  \\\n",
+       "Samp_1   -6.925227 -2.520610  10.380707 -6.079539  ...  10.465338  1.922512   \n",
+       "Samp_2   -6.747382 -2.712280  10.841243 -6.340821  ...  10.551755  1.800501   \n",
+       "Samp_3   -6.436741 -2.377240  10.565243 -6.044507  ...  10.307515  1.991552   \n",
+       "Samp_4   -7.229847 -2.609629  11.160280 -6.466545  ...   9.915145  2.137159   \n",
+       "Samp_5   -6.868394 -2.698423  10.211682 -6.265595  ...  10.592827  2.231166   \n",
+       "...            ...       ...        ...       ...  ...        ...       ...   \n",
+       "Samp_354 -6.723627 -2.290536  10.847353 -6.341079  ...  10.186933  2.077744   \n",
+       "Samp_355 -6.955723 -2.971791  10.294231 -6.077254  ...   9.624207  2.077401   \n",
+       "Samp_356 -6.739620 -2.504380  10.865619 -6.296479  ...  10.497804  1.984929   \n",
+       "Samp_357 -6.424579 -2.768089  11.026826 -6.542514  ...  10.566136  2.141326   \n",
+       "Samp_358 -6.906523 -2.443945  11.551972 -6.444730  ...  10.345094  2.132126   \n",
+       "\n",
+       "          Gene_280   Gene_62     Mir_21  Gene_495    Mir_59  Gene_340  \\\n",
+       "Samp_1    5.955446 -2.626381  13.947213 -2.806483 -0.553959  1.726373   \n",
+       "Samp_2    5.912429 -2.548239  12.697251 -2.648551 -0.624136  1.708398   \n",
+       "Samp_3    6.038545 -2.479833  13.769784 -2.652630 -0.592947  1.583839   \n",
+       "Samp_4    5.804663 -2.629407  13.458064 -2.546526 -0.499689  1.648553   \n",
+       "Samp_5    5.314841 -2.462155  12.673634 -2.597200 -0.638230  1.709379   \n",
+       "...            ...       ...        ...       ...       ...       ...   \n",
+       "Samp_354  5.888022 -2.566317  13.658852 -2.439809 -0.526501  1.692860   \n",
+       "Samp_355  5.623264 -2.663072  14.311062 -2.595351 -0.584500  1.673485   \n",
+       "Samp_356  5.467676 -2.442813  14.854524 -2.540575 -0.527353  1.648124   \n",
+       "Samp_357  6.178911 -2.667312  13.193234 -2.821949 -0.585463  1.659515   \n",
+       "Samp_358  5.578996 -2.523854  14.942860 -2.537228 -0.534264  1.689256   \n",
+       "\n",
+       "          Gene_291   Gene_81  \n",
+       "Samp_1   -2.651651 -2.406555  \n",
+       "Samp_2   -2.596500 -2.342931  \n",
+       "Samp_3   -2.653498 -2.446277  \n",
+       "Samp_4   -2.520416 -2.530145  \n",
+       "Samp_5   -2.481453 -2.397491  \n",
+       "...            ...       ...  \n",
+       "Samp_354 -2.711183 -2.466377  \n",
+       "Samp_355 -2.578159 -2.418925  \n",
+       "Samp_356 -2.674988 -2.482001  \n",
+       "Samp_357 -2.625582 -2.425626  \n",
+       "Samp_358 -2.751487 -2.255327  \n",
+       "\n",
+       "[358 rows x 600 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(enhanced_omics_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.utils import evaluate_rf_regressor\n",
+    "from bioneuralnet.utils import plot_performance\n",
+    "\n",
+    "y_phenotype = phenotype.values\n",
+    "X_enriched_omics = enhanced_omics_df.values\n",
+    "X_raw_omics = merged_omics.values\n",
+    "\n",
+    "accuracy_with_embeddings = evaluate_rf_regressor(X_enriched_omics, y_phenotype)\n",
+    "accuracy_alone = evaluate_rf_regressor(X_enriched_omics, y_phenotype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_performance(accuracy_with_embeddings, accuracy_alone, \"Raw Omics vs Enriched Omics\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Mir_2</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gene_1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gene_2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Gene_6</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Gene_7</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Omic  Degree\n",
+       "Index                \n",
+       "15      Mir_2      14\n",
+       "1      Gene_1       4\n",
+       "2      Gene_2       4\n",
+       "3      Gene_6       4\n",
+       "4      Gene_7       4"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Mir_53</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Gene_5</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gene_54</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Gene_88</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Gene_174</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Omic  Degree\n",
+       "Index                  \n",
+       "7        Mir_53       6\n",
+       "1        Gene_5       4\n",
+       "2       Gene_54       4\n",
+       "4       Gene_88       3\n",
+       "5      Gene_174       3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Lets take a quick look at some of those clusters from SmCCNet\n",
+    "from bioneuralnet.utils import plot_network\n",
+    "\n",
+    "cluster1, cluster2, cluster3 = smccnet_clusters\n",
+    "\n",
+    "cluster1_mapping = plot_network(cluster2, weight_threshold=0.1, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster1_mapping.head())\n",
+    "\n",
+    "cluster2_mapping = plot_network(cluster1, weight_threshold=0.009, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster2_mapping.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.clustering import CorrelatedLouvain\n",
+    "import networkx as nx\n",
+    "\n",
+    "merged_omics = pd.concat([omics1, omics2], axis=1)\n",
+    "G_network = nx.from_pandas_adjacency(global_network)\n",
+    "\n",
+    "louvain_instance = CorrelatedLouvain(\n",
+    "    G=G_network,\n",
+    "    B=merged_omics,\n",
+    "    Y=phenotype,\n",
+    "    tune=True\n",
+    ")\n",
+    "\n",
+    "louvain_clusters = louvain_instance.run(as_dfs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(louvain_clusters))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Cluster</th>\n",
+       "      <th>Louvain Size</th>\n",
+       "      <th>Louvain Correlation</th>\n",
+       "      <th>SMCCNET Size</th>\n",
+       "      <th>SMCCNET Correlation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cluster_1</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0.724029</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.134853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cluster_2</td>\n",
+       "      <td>76</td>\n",
+       "      <td>-0.124891</td>\n",
+       "      <td>63</td>\n",
+       "      <td>0.731769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cluster_3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.069064</td>\n",
+       "      <td>15</td>\n",
+       "      <td>-0.085057</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Cluster  Louvain Size  Louvain Correlation  SMCCNET Size  \\\n",
+       "0  Cluster_1            77             0.724029            67   \n",
+       "1  Cluster_2            76            -0.124891            63   \n",
+       "2  Cluster_3             6             0.069064            15   \n",
+       "\n",
+       "   SMCCNET Correlation  \n",
+       "0             0.134853  \n",
+       "1             0.731769  \n",
+       "2            -0.085057  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets comapre these clusters against SmCCNet clusters\n",
+    "from bioneuralnet.utils import compare_clusters\n",
+    "\n",
+    "compare_clusters(louvain_clusters, smccnet_clusters, phenotype, merged_omics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Gene_222</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Mir_2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>Gene_1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Gene_6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Gene_7</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Omic  Degree\n",
+       "Index                  \n",
+       "34     Gene_222       7\n",
+       "30        Mir_2       7\n",
+       "66       Gene_1       7\n",
+       "45       Gene_6       6\n",
+       "53       Gene_7       6"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Omic</th>\n",
+       "      <th>Degree</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Gene_187</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Gene_464</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Gene_83</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Gene_354</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Gene_300</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Omic  Degree\n",
+       "Index                  \n",
+       "30     Gene_187       9\n",
+       "44     Gene_464       6\n",
+       "21      Gene_83       6\n",
+       "16     Gene_354       6\n",
+       "9      Gene_300       5"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.utils import plot_network\n",
+    "from bioneuralnet.metrics import convert_louvain_to_adjacency\n",
+    "\n",
+    "cluster1 = louvain_clusters[0]\n",
+    "cluster2 = louvain_clusters[1]\n",
+    "\n",
+    "# Convert Louvain clusters into adjacency matrices\n",
+    "louvain_adj1 = convert_louvain_to_adjacency(cluster1)\n",
+    "louvain_adj2 = convert_louvain_to_adjacency(cluster2)\n",
+    "\n",
+    "# Plot using the converted adjacency matrices\n",
+    "cluster1_mapping = plot_network(louvain_adj1, weight_threshold=0.1, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster1_mapping.head())\n",
+    "\n",
+    "cluster2_mapping = plot_network(louvain_adj2, weight_threshold=0.1, show_labels=True, show_edge_weights=True)\n",
+    "display(cluster2_mapping.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>phenotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>235.067423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>253.544991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>234.204994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>281.035429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>245.447781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>236.120451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>222.572359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>268.472285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>235.808167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>213.886123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           phenotype\n",
+       "Samp_1    235.067423\n",
+       "Samp_2    253.544991\n",
+       "Samp_3    234.204994\n",
+       "Samp_4    281.035429\n",
+       "Samp_5    245.447781\n",
+       "...              ...\n",
+       "Samp_354  236.120451\n",
+       "Samp_355  222.572359\n",
+       "Samp_356  268.472285\n",
+       "Samp_357  235.808167\n",
+       "Samp_358  213.886123\n",
+       "\n",
+       "[358 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>phenotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Samp_1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_2</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_4</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_354</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_355</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_356</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_357</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Samp_358</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         phenotype\n",
+       "Samp_1           1\n",
+       "Samp_2           2\n",
+       "Samp_3           1\n",
+       "Samp_4           3\n",
+       "Samp_5           2\n",
+       "...            ...\n",
+       "Samp_354         1\n",
+       "Samp_355         1\n",
+       "Samp_356         2\n",
+       "Samp_357         1\n",
+       "Samp_358         1\n",
+       "\n",
+       "[358 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "phenotype\n",
+       "0     38\n",
+       "1    158\n",
+       "2    141\n",
+       "3     21\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.datasets import DatasetLoader\n",
+    "import numpy as np\n",
+    "\n",
+    "loader = DatasetLoader(\"example1\")\n",
+    "omics1, omics2, phenotype, clinical = loader.load_data()\n",
+    "display(phenotype)\n",
+    "\n",
+    "min_val = phenotype[\"phenotype\"].min()\n",
+    "max_val = phenotype[\"phenotype\"].max()\n",
+    "\n",
+    "# linspace creates an array of evenly spaced values\n",
+    "bins = np.linspace(min_val, max_val, 5)\n",
+    "\n",
+    "phenotype[\"phenotype\"] = pd.cut(phenotype[\"phenotype\"], bins=bins, labels=[0, 1, 2, 3], include_lowest=True)\n",
+    "count_values = phenotype[\"phenotype\"].value_counts(sort=False)\n",
+    "\n",
+    "display(phenotype)\n",
+    "display(count_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DPMON (Disease Prediction using Multi-Omics Networks) reuses the same GNN architectures but with a different objective: \n",
+    "- Instead of node-level MSE regression, DPMON aggregates node embeddings with patient-level omics data. \n",
+    "- A downstream classification head (e.g., softmax layer with CrossEntropyLoss) is applied for sample-level disease prediction. \n",
+    "- This end-to-end approach leverages both local (node-level) and global (patient-level) network information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.downstream_task import DPMON\n",
+    "dpmon = DPMON(\n",
+    "    adjacency_matrix=global_network,\n",
+    "    omics_list=[omics1, omics2],\n",
+    "    phenotype_data=phenotype,\n",
+    "    clinical_data=clinical,\n",
+    "    tune=True,\n",
+    ")\n",
+    "dpmon_predictions = dpmon.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual</th>\n",
+       "      <th>Predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>358 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Actual  Predicted\n",
+       "0        1          1\n",
+       "1        2          2\n",
+       "2        1          1\n",
+       "3        3          3\n",
+       "4        2          2\n",
+       "..     ...        ...\n",
+       "353      1          1\n",
+       "354      1          1\n",
+       "355      2          2\n",
+       "356      1          1\n",
+       "357      1          1\n",
+       "\n",
+       "[358 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(dpmon_predictions[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bioneuralnet.utils import evaluate_rf_classifier\n",
+    "\n",
+    "X_raw = merged_omics.values\n",
+    "y_global = phenotype.values\n",
+    "raw_rf_acc = evaluate_rf_classifier(X_raw, y_global)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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R7EeS6qZdQbpA8Dcd0/6aboYalRWpJ06lL7pIz/Q9KvH2A7R1P47a4Bhr2P0YaGgk/gCqUfe2by1VvX7wDqgq+RB1fwdvnKMWMk2Td/vttyceqznzjwZLqrVQgy/V2qg62WA5hv9jquRSLZvqIQibmSVV6YWmI1Xd7RNPPGFHHXWUG2gZpJpqrb9qdDVbjVpC9Qc+09eqLyof8OMfzj777GoDPdV6rORK1MqohEsztGhdVWqhXhdf2uLnhldLbLKZUPScpqyszfz2mv5Qrcv67HSPhrAyn9quW12ofEPzwKt2Xz1V2o98i2xjUMu7LoS1be644w53casbmkWl0jmNgVEplRJzLaPm4FmNATjllFNcj5u2r+bnr02Phi/50X6UbkagMBxjDbMfA42Fwb0AqtEfPNUAa+o7tY5pLn+1pOoPvWbvUQIyadIkV3+vea5Ff1BV96okSL+vlr5kZT9K5lXqoz+kStCU/Hv6XQ369b0KmZT5BGmQpOpwdYMxlRloxiK14ukurFpHratv9VNr7X//+9+MbkZU37RuKt/QdtUFkVqOdYMmJYLaFkrQ1FuiGxBJly5d3HtT668GICoZ1HbTYEuVKainRYlWzTuLqidHreJq8VV9cqaU6GvdlGgr+Us2QLS26xZFzc9Jy9I20nJ9y6v2L71uJjO+1AftR2qt17ZRHbwvfYtCiabuXKsWfLXCa3yN6sn94F9dYGvciyh5HThwoJtutLZ0nKnkpy538+UYq/1+DDQ1En8Ay1DL/kknneS6vHUjLt3RUkm/Eg6V8+i29xMmTHA1sBo4p5k61MqpOf7VIqx4/XE98sgjqy1XFwt+YGGwzCdY7qNeBt87UFubbbaZ+wOvGtxXXnnFrataTVUaoT/YSlpVdqCBq9lArbcqE9G2U4+HLqy0rl27dnUlBwceeGC1mU302WjGI20rJZy6G6qm3tQg6d12283Vjmcyg0kU+uw1AFtTMwan8Kypodat5h1ftT2072lfVUmYWmmT7VONQcmtEnbJZHYeT70EGhivsh8dXxrnogRTybMuIlQmo+2vfbY+Emgdpzo+dCFYWxxj9X+MAY0hr7K2/X0AgJyille1xqqcBAAQP9T4A0AMqP5dZRlqxQQAxBOJPwDEgEqsNIe7n0ISABA/lPoAQAxo+kVNkao5yQEA8UTiDwAAAMQApT4AAABADDCdZwP67LPP3HzlmiIsOF85AAAAUB90/wxN4KCpgdPd8Z7EvwEp6VcllW4uozsZAgAAAA0heJfpMCT+DUgt/Ur6dSdT3WgGAAAAqE+6q7QamoM3oQtD4t+AVN6jln4l/brbHwAAAFCfvvzyS5dvRikrZ3AvAAAAEAMk/gAAAEAMkPgDAAAAMUDiDwAAAMQAiT8AAAAQAyT+AAAAQAzkzHSeU6ZMsSFDhtgHH3xgs2fPttVWW8323HNPO+GEE2yFFVaItIwjjjjCxo8fnzZuzTXXtNGjR9fDWgMAAADZIScS/08//dSOOuooN0fpZptt5m5H/OGHH9qwYcNcgv7QQw9ZixYt0i5n2223tVVXXTX0+ddee829RpcuXSwb6GYM+gKQ/XSjPn0BAJCtCnPh9sOnn366S8gHDhxo/fr1c48vWLDAzjjjDJf4Dxo0yC655JK0yzr55JNDn3vsscds1KhR1qlTJ/c6TUFJ/u+//27z5s1z71d3/QWQO4qLi10jRJs2baygoKCpVwdY7tx+++32wAMP2J9//mm9e/e2Sy+91FZccUV7+eWXbfDgwTZt2jRbe+21XX6w8847J12G8ofrrrvOXnzxRSsrK7Pu3bvbxRdfnGgYvOuuu9zr6Hg+//zzrW/fvu7x8vJyt8x//OMfdswxxzTq+wZiU+P/3HPP2fTp061Xr16JpF9KSkrsyiuvtObNm9vIkSNdslxbX3/9tV1++eXujmc33nijW2Zjq6iosBkzZrj3quSfpB/IPUoMVIr4ww8/cAwD9UzJuBr6CgsLbY011rBnn33WNdRNnDjRJfpTp061LbbYwn3/17/+ZZMnT066nJtvvtnuv/9+l0ess8469uqrr7rfl19++cVdFJxyyimunPjCCy90f599A+GiRYvs4IMPbtT3DcSqxX/MmDHue58+fZZ5rlWrVtajRw8XM27cuMRVeabUYqCr/rPPPtvWX399awpz5851X9K6dWvXaqgLEUoHgNyg5ECtkD///LNrUdQFQLt27Zp6tYDlgnr/77zzTltllVXsmWeecX8fd999d/vyyy/t7bffdgn5ZZddZvvvv789/vjj9p///MflBeutt94yy3rzzTddA9/TTz/txgj68X9qQPzxxx/dsazef8XomJ4zZ461bNnS9QQcffTRVlpa2iTbAIhF4q/WeNlggw2SPq+DU4m/rvhrk/jrBPL++++7k4MO6IYq4fEtBjXl51d1uvz666/uu05qKhNIJngRkK72n9hl44kltiFj1Qqp5ECx6r1TEtG2bduUx72EnRuIbZzY4NiMdOOqiG262K+++so1jinZV0KuOD8Jx5NPPpn4/eDnraRe/6+53JVXXtn1Coie12O6kNAxvPrqq7v955tvvnE98CojUrxeQ8f0IYcckniNhtoO2XJsEJsbx4Z/bLlJ/NV6JmGDcpUo++65TKkrXjWBom7BoqIiawjqTfj222+XeVwnL3VX6gNTjHY+dT3q52Q7qeoNgyUFYR90JrHakXTCq02sWmDCDphMYkXvuzaxauVJVVIRXIf6jNX29QdhJrGKU3xjx2rf9jXn2rbaxo0dqz+q+hLtY9rXGjtW6+qP80xiJdlxmSzWJxt//PGHKzWo2WunREKzknnJzg01zxHB2c3Cjk+1QmpGMu/7778P3S+1r6sO2lNpUtj+o/2sffv2if+rhjpsu+lzUOmEp8QpbLtpm6277rqJ/+tiaf78+UljtQ2DLbc//fSTGwcVJthzq78N+izCdOzYMfEZKVallmG0vn5/nzVrVqKXNpkOHTok9gn1/vz222+hsdq+/pytRiC1MIdZa621EudALVPLDqP9wbdOK2mdOXNmaKwSXj9DnrZBqr+p2n+1H4taxPV5hFGv10orreR+1memzzmM/p7r4lnUa6b9x9OEHqJtrlb97777zrbffnvXY7/LLrvYVltt5X4eMWKETZgwwTbccEP3peNLvej6Ep2j/v73v7vGwr333tu9DyX5xx57rGvtV5J/zjnn2NChQ93np5IfHcf6/1577eW2od+OWlefg+iY1/EZRr34Po/RMZzquOccUYVzRPRzRKq/TzmX+PsPOZjsBfnHU33AYV566SV31d+5c2fXitBUggdp8CoUQO7hGAbqn09s3nnnHXfxoyRaf8N1gXD11Ve7xFs/f/TRRy5OCXnYsegTWCX6PiH2FxyiiwB96SJByfHYsWNdorXvvvu6cQaqMtA6aGygT/yBXJFXmeXzRWpqTR18quHzV+xBurq/6KKLXOLuW++jOvDAA91UoTfccEOtxwekotpDXZDopKKWh2R0YtIJS60PoouQsJNVNpTO5FpsMJ5YYhsjVi1oKlHU74Qdz9nQfU1s7nTjE2tuBh4NwFXC/fzzz7tW1b/97W+uNfzUU0+1W265xQ444AA777zz7NZbb3X1+Jp9R2P3gsvV/rDbbru5lmaNGVDLr5J8tZQ/9dRT7pgNroPi99lnH9e7oFmENLW4JhbRa3Tr1s2uuuqqet8O2XJsEJsbx4YvhVNDuXqANtpoI8vpFn91Par7I6wbQ1f4kulMPOq2UtKvq/xdd93VGpI+rKitgFHnAs9k0C+xxBLb+LH+uE937GfSQ0Bsw8ZGPf8S2/ixKkUSlYn4skwlOEr8P/nkE/d/zfynkhqVAinxf++995b5/NVy76f83G677dxjO+20kyvn0Q1Cg+MJ9fq6v48qA3QRoVmERCVCGjj8xRdfNPh2yJZjg9jsPTb8Y1FlfZ+0nxUjrDbR1yFmOnuGWg9kjz32qFYPDwAAsouSfDXwqX5fNdbJ6uTVyx6cFCTZ4HotQ3Xmyh38+AjV+Euysh3dKFQ9CcHnolzQA9kq6/dcf/XtD8yaJk2aVC0uqtdff9191zy9AAAge2k83+GHH+5a7FV6079/f5foq8xXpTpq8VStv0pxdNMtX87r7wekQbqar1+Jv0p7VUWgHgLFKB/QANltt9222mvqcV1EHHfcce7/vmT3888/dz0EG2+8caNvB6Cusr7URzV1uqOu7sqn7rsgjWx+9913Xbdfz549Iy9TA3vURacawc0339xyyfjxlpO6d2/qNQAA5LLTTz/d1WPrpp0q81VOoPn6NQuOavyHDBniyn5UFnTCCSck7tyrngGV7GjmH/GDclW6o+dU8nPBBRckZjXyVMeviww/a47yjMMOO8xdCGj2ltNOO60JtgKwnA/u1cGtq3MNxNFUXf6OeXr8zDPPdAezbr6hu+t5GgysGn4/BVLNaTo1CEKj89VLoHn8G4of3JtusEVwcK/WKVUXohL/QYP0Hi0naNOfdVb9J/664DvyyCNDn9dFnU7i+vw1kEutQLl00xVNXffEE0+4GlV1SWufVjmb7kq53377uYFmDU13t9QfU21n/VFENJkczwAANFa+mRMt/ureU/edrrAHDBjgZvHRqH5N2aWESLP++FttB+f+97P06MJA8UEa2CO5Og2Xkv5cSfwbmnZyzeFckw4AXfypS1Zf2g/uu+++rE/+td5KsjVrhWjf3WabbdzPmrdaLVT60mC0a665JjE/NgAAQDpZn/iLpsx67LHHXOujbqutun4lRAcddJAdc8wxy3TPpeNvfEDSlPtatWpl1113XejzmnlBtZ2awemBBx6w448/3rKVbnii/fnjjz923ciXXHJJIun39D4uvvhiN4+0alN1a3p/I536pi5tXUDrhjZAo5qVozWNANA2u2ubcyLxF82tG3Wefl0U+K72ZHTBoC8s/1S7qQT27rvvdq3+2Zz4Dxo0yCX92tcffvjhpAl9165d3XMqXVItq8rfrr322gZZn+DdLoFG9+Ugs0q6NgHkiLwis43OsmyXM4k/UFu+1CvZ7bA1W9Tw4cNdLb1KxHTzJU0B1717dzc4zN8CXMm1bvZy4oknurElQWeddZYrv1HJkW7rHqSLVQ040ywTRx99dOg6arpaJfRy2WWXpWzFV7mSbiCjuaQ1RuWf//xn4hbounnNk08+aY8++qi7ZbkueDQrhUrmevXqZeeee64bK/DKK6+496MLZPV8qVdNt6kP3iY+VY2/Su1UOqV5r7VdNZhOM2KcdNJJidvSi26Dfscdd7g7X2oubA0p0m3Kde8MbY+G6q3AckBJfwWJP4AckW85IUdWE6g9lcVIzbsnqwdA07mpjExjBXbYYQfbeuut3RzRuoOj5m7W7dpFNfW+dChIiazuKi3vv//+MncAVMIrycYh1IzT9HIq8dEA3nR0t0k/I9XTTz+9zPO33Xab/fvf/3YzWCkh13rq4kSlRHpOd7rUgGH1iKjESM8deuihoTfKC7r//vtdL4rGIWicjGbe0vIfeughN8WeEnzRsjT9nubBVnldjx493NePP/7oLio0KF/rAAAAGgct/ljuKAn9888/3TRtas1/88033ZSvaq33lHCqVl7fr7/+ettrr70Sz82bN8/dpVH19BpMrpbwLbfc0o0n0Mh5TSOrn/0MUboJjGYRmjt3rvu/n9tZrfi62YxKd3SXyFT8nSdVyhOVkmi1vAfvHumNHj3aDYrXDECi1n/drE7jY/R+VVakHgPx82Jr5izNW92nT5/Q19T7HzhwoHu/mupuxx13dI+rp0RT5KnXQj0WauV/6aWXXLx6T9Tz4GfX0nbSRYa2jXoe/EB8AADQsEj8kdOUrKa7eZvKYJSUBqe4UrKu0hclsMGkX1T6oqRYib+WL5qSUS3bKqP53//+l/idt956K3EjOLWaa5pRn/gridZFSLrWfpk1a5b73qZNm8jv3d+VUkl9TUq2fdIvKuFROc8bb7zhpgL1Sb+ojl+9Auo50MxBqSixVy+CLqJ80i/ajiojeuedd9zFlHoRVDrlXzs4pW7Lli3d2AS9FjfAAQCg8ZD4Y7mZzlNJtlrjlXyqBVqt57q5i0pidFfHICWjmg6zJk0Rq5p41a5LsBRF5T5K/FXu4xN/XQSoN0E9BD7xVzlN8O7QURJ/ra8UFxdHfu+67XyYZDem8wN1N9lkk2We8zNcpSv10fsT1egnG3vw4osvVuuREI1D0Oei+ymotGjNNdd0JVX6AgAAjYfEH8vddJ5Tpkxxs/eoxf722293A2xr3sTNU32+bpSlEh3Vps+fP9897i8Ugve3U9KqxFzJvk+SVdevmny1XKsFXoOElcSr1l+9ARroqntNpKPBscGpZqNQKZEEB+QGW9Vr8u/Jlykley4dXRiJv5NlKrrwUjmVLrBUbqUv3wOjCwfdjC9dCRQAAKg/DO7FckcDZDWAVb0BqnVX8lmTEnMNftXMMqNGjXKlPKpt183g7rrrrqS/o/tFqBVbpTWqlVevgJJ/3cZdNOe+ZrFRzb0uCDTOQLeMj5JU+4sDTecZlY9Ndpe+VL0BdaEyn0wuFDQIWD0fmoVI5VC6OFKJj2YU0v/94GcAANDwSPyxXNI0nJraUtSi/9xzz1V7Xsm+ylLU0q6fVd+ulmlNR6mW/bCSl+DsPiopEp/4++963M8kFKXMR5QEq2RIZUYffvhh2njNNuRnE9p3332tsfi7Xc+YMSPp8xqsq9l+VNrj6QZg+++/v914442uF0TbWttFZVQagAwAABoHiT+WW3//+98Tyfh///vfasmoT66VcHfq1GmZ39UgWKk5PadP5JXAqqxH89D71nr/WqqDVyu37yGIokWLFombi2nOfM0sFEYXJRpIq3XTTD0dO3a0xrLVVlu57/7CpmZvgHpK1GuimXs0ZacGAOvCKkjTqmrsRaoLCAAAUP9I/HOQytVz6aspaWpJ3bxKtfOahtLzde5K4H1dv2g2Go0Z8HX8+n+Q6ulVz6+k/7PPPnMz5WhGG9Gg1fbt27sWf5Wz6L4AmQzWPfnkk91sPJqGVHeW1mvUpLEImv9e03iqPl6zFTUmvbbKolQO5Qf6isY1aPtqO+s9qI5fN05TWZRuAubHI3j+3gObbrppo64/AABxxuDeHHRW9t8ROmsoEdedbTVvvW7KpbIYTV2pxPrBBx90d65VK75mwVGLtebT151oNfe+ym5qJqy+3Edz0Adb+T39X3fNzaTMJ1iXr4RavRNahm5+pfVXj4SSbV1M6E7DoqlFr7rqqsRsPI1FA3Z1XwOVRR111FFuYLPq9jVfvwZHqxToiiuucLG6N4BKrDSoVzP66F4I6iHR+IjJkye7n33LPwAAaHgk/jmme/emXoPcc+yxx7qpNpXkDxgwwJWeaFaaxx9/3G666SZX9qPSHg0GVpKt+e91R1/N86/kXwm3WrA9DdgdMmRIYkBvkP6vpF1JfHCe+6jUQ6BeCtXE6+ZhGiSsxFnL03gE3RlX66ZW9abcnpoS9J577nE9D5o9qV27dm4g7ymnnJK4v4B6QtTaf++997rxFNrOurjSTEea0eeEE05wvSQAAKBx5FUG5ytEvVIr6F9//eUSymQzr3iq1VZSKroZlVp3AeQmjud6MGu82YSBZhVL76MBAFktv8hs4/PM2nbP2nxT+IsEAAAAxACJPwAAABADJP4AAABADJD4AwAAADFA4g8AAADEAIk/AAAAEAMk/gAAAEAMkPgDAAAAMUDiDwAAAMQAiT8AAAAQAyT+AAAAQAyQ+AMAAAAxQOIPAAAAxEBhU68AMjRrvOWktt2beg0AAABijcQ/F305yKxyoeWEvCKzjc6q98W+++67duSRR0aOV+wFF1xgDeWII46w8ePH25AhQ2zXXXet9fvZcMMN7emnn26QdWzIdaioqLAXX3zRfX366ac2e/ZsKygosLXWWsu22WYb+/vf/26dOnWyhlbXzwEAgOUZiX8uUtJfkSOJfwMXkzVv3tx22WWXtHFdunRp2BWJsR9++MFOO+00++qrrywvL89dOHTt2tX++OMP+/rrr+3++++3Bx54wE4++WQXl59PhSEAAE2BxB85rVWrVnbdddc19WrY1VdfbfPnz7fVVlvN4mTq1Kl20EEH2a+//mq9e/e2//znP9ahQ4fE84sXL7aXXnrJLr30Uhs6dKh9//33dv311zfY+sT1cwAAIAoSf6AerLHGGhY3lZWV9u9//9sl/f369bOrrrrKtfgHqdynb9++rself//+9txzz1nPnj3twAMPbJB1iuPnAABAVPS5I3ZUy77BBhu4mv8ZM2bYeeedZ9ttt51LTvv06WM33nij/fXXX9V+5+abb3a/8+STT7pW5a233tq22GILV77ia8v1/KuvvrrM6z377LN21FFHuVr3Lbfc0vbdd1+78847Xct0MtOnT7fzzz/frdOmm27q1ummm26ysrKypPEffPCBnXrqqbbtttu697DTTjvZxRdfbD/++GPSeCXq1157re22226uJGf33Xe3u+++29XpZ+K1116zL774wlZeeWX3ejWT/qD27dvbmWee6X5W/X3wtXbeeWf3PhcuXGi33367Wx/9X+9DvQN6fNGiRe65PfbYI7HOem7BggXVXqc+Pgf1Smif0Gvotbp37+6W+/jjj7uLHQAAchUt/oh1bbpaqlWOstlmm7mkThcFt956qxugqmS4pttuu82mTZtmvXr1srlz59q6664bunwt76yzznKt3EVFRe5iQWMSlKgr8R4zZozdc889VlxcnPidmTNn2v7772/l5eUu4VRS+v7777syGSXZSn6D7r33Xhs4cKD7eZNNNnGv8c0339ijjz7qSmzuuOMOl7x6P//8sxvA+91339mqq67qynP0mC5mMh18q+WLEmS9r3T22Wcf1yugiy1tZ7X8B7eVLqLefvtt9741KFgx2t4aKDxnzhx788033cWWLiL+97//uedUanTDDTekfN1MPocpU6a43ojff//dOnfu7LbPvHnz7L333nODhvUZ6CIHAIBcROKP2FIit8MOO7jkT63WooT/0EMPtbfeess++eQTd0EQpMRQrcTbb7+9+3+qVvIHH3zQJZtKYu+66y5bZ5113ONKKo899liX0A8fPtyOP/74xO8oyVXie8stt1jLli3dY0pQDz/8cHv99dfdYFklpKJkVEn/Siut5C4MlNB6Wu6VV17pBtMqQS8pKXGP6zEl/XvttZf7XX/RoRZyle1kQttHghcWqay44oru4uSjjz5yCXQw8Vervra9WtU1OFheeeUV15MxcuRI9/mot8VfnGjbHXbYYfbCCy/YRRddZK1bt66Xz0HP6/ETTzwx0UMhX375pR188MH28MMP20knnWTt2rXLaFsBAJANKPVBTlNZjEo70n2FueyyyxJJv09iVQYiSrJrWm+99RJJv6SaoUYz2YgGtvpkU1q0aOFKSTQIVi38NalV3Cf9stVWWyXWSQmop9Z8tWafffbZ1ZJ+UUmLLmp++uknGzVqlHtMr6WLACXgl19+ebWeBk19ecghh1gm/Lq3bds28u+sssoq7rvWqyaV0/ik369TaWmp+/kf//hHtR4Jvd/VV1/dvX/13KSSyeeg3g9Ze+21qy1jo402chdN11xzTbXtBgBALqHFHzkt6nSeyShx1FdNvjU3WQ2+EsAofvnlF9c7oPVT7X1NSuZffvnlZR7XbDRqmU62rqLWaFF5knosJNhyHqQylTfeeMPeeecdV76i0hklyt26dbMVVlhhmXiNJdDUm1H53g6Vz0RVWBh+ytl8882r/V9jBjRrkz6HZNOxqqdDZUNhYx9q8zn06NHDbTNdEH744Ye24447uu2rCzH1kgAAkMtI/BHb6TyVOKZKTpOV8ej1olDC6RP5TOatT7dOGuQqv/32W+LCJN2NqvwgX9+aHTbVZc1W7nS0HJUNqf4+Kt+ynmwdgr0cnh8wnGy7pxpMXNvP4eijj7bJkyfbE088kfjS76nkSxdGmrpUPSYAAOQiEn/EVpTEsba/4xP0TF8j6kWCvyjRBcGee+6ZMnbNNdes9v+wmWk09WYm1AqvxP/jjz+2v/3tb2njNWBZtf1hPSeZ9BxElennoO2pUisNNNYYAw0iVsu/xiXoS4OpVedfc5sCAJALSPyBBhCsZVeinSzxfOihhxIz62RK4xKUKKvk54orrrBmzZql/R3fyq5xEcn4HoGo9ttvPzdFpr7OOOOMtC3hzzzzjJsmVesRVp7U1J+Dv/jRzEEaV6AvDTxWWZVq/CdNmuRmVtJ4AQAAcg2De4EGoBZh1eX/+eefiVr8oK+++solj5dccknGLe2ipF9TW6rlX9NRJqMpOjVX/X333ef+r2Rbr6XZgJKV54wePTqjddB9BlQTr7KjAQMGpJzhSNNuavYkOeGEEzIqf2rMz0HrpvcUHHysba3pW3URIBpXAABALiLxBxqIZqkRJZXBZFHzwusxUc14bflEVANRNXA3SANWNVBXia0fGKspL3UhoJtenXPOOfbHH38k4vX7ye5bkIpazzXLjVrV1eqv8hgl+DVpqlDNGKQLBNXJaxrObP0c2rRp49ZTU52qNMnTz5o6NJPpSwEAyDaU+uSivKLcuWTTujYg3YVW01lGUdtBwLWlgaKqD1fiqzvOan5+tSqrVlzJpaakVAtzbak05ZRTTnFz+OumXBtvvLGbEUjJt5/2U9vGTwUquiOwpikdN26cGxSsGX60LuoFUA+C1jcTKtvRAFjdA2Ds2LHuXgN+PXxNvwbYqoVf7zXTewU09ueguft1EzEl+Zrf3180ff75525gsu6hoKlSAQDIRST+uWijs5p6DbKGasb9PPXZlvgrubz55pvdTan0pURS9eKaS16t9UpI6zonvBJpJbIq59EgW921Vy3wO+20kx1zzDGubKXmrEGa1153qtV2U6KuVm7dsEp3DN5tt90yXgdNf6qbZKmXQS3/uhGXLi40ZahKbTQWQHdI7tixozWFTD4HbTvd9XjYsGHu4khfGvCrmn/1WmibRrlLMQAA2SivMmyKD9SZWl2VmCpRSDX/u2qjJ06c6H7WzaYaq/4ZQP3jeK4Hs8abTRhoVrGwqdcEAKLJLzLb+Dyztt0tW/NN4S8SAAAAEAMk/gAAAEAMkPgDAAAAMUDiDwAAAMQAiT8AAAAQAyT+AAAAQAyQ+AMAAAAxkDM38JoyZYoNGTLEPvjgA5s9e7a7Y+iee+7p7ripGwVl4s8//3Q3MHrxxRfdXU41z7buNqo7cvbp08caW15eXrU5wJn3G8hdwVujBI9tAACaWk5kmLoTaP/+/d2dRnVnzd69e7sbFejumgcffLD9/vvvkZf1yy+/2IEHHuju5Pnrr7/adttt526yo7t5nnbaaXb//fdbY1NyoLuDyoIFCxr99QHUn7KyssQdg0n8AQDZJOsT/4ULF9rpp5/uEv2BAwfaiBEjbPDgwfbqq6/azjvvbF9//bUNGjQo8vIuvPBCmzx5sustGD16tOtFeOSRR+yuu+6yoqIi9xo//fSTNbYWLVq477oY4WbKQO7yDRGZ9kQCAGBxL/V57rnnbPr06darVy/r169f4vGSkhK78sorXfI/cuRIO/PMM22llVZK23Pw+uuvW4cOHeyaa66x4uLixHNq+dfyx40bZ5988okrJWpMLVu2dEn/H3/8YdOmTbNWrVq590jZD5AbFi9ebPPmzbM5c+ZUu5gHACBbZH3iP2bMGPc9We29kuMePXq4GCXsffv2TbmsF154wX1XLX8w6fcuv/xyayqlpaW25ppruoscJf/6ApCbVl55ZRJ/AEDWyfrEX6U8ojr8ZDp16uQS/4kTJ6ZN/D///HP3ffPNN3elQy+99JJ99tlnrqVu0003tb/97W/WrFmzen8PKt3RoN1kgi36K664orVv3961GqpcYNGiRdVig/XC6cqBiF02Nl08scTWNVbnDyX96n1UfNjvBI/7sHNDXGOXbndtu8BAaQsc94HHk8kkNhhdFZkqnlhiGyO24fZ3YhvyHFH1eCI2xd8AF5eXlzjf1TU2kxLxrE/8f/75Z/d91VVXTfq8Bvv6QbvpfPfdd+67ZgXSQF61rnuq89dg4dtuu83WW289q+/Bft9+++0yjzdv3tzWWGONajMX+Q9PfxA15iDYIxCM1XvRBUsySj7WWmutxP+///77ZS4iPPV8rL322on/a5aj8vLypLEagKwyKU8lSX4gY00a2LjOOusk/v/jjz/a/Pnzk8ZqZ+7YsWPi/zNmzHAXZmGCn4/2j1S9I+uuu24iudA+kmoguNZX6y0zZ850F2BhdIHmP59Zs2bZ3LlzQ2O1fX0Pk8pAVNIVRr0+KvESxfmykWS0P2i/EL2+1iOMStd8zbnel95fGB1ruggVbVt/DIYdf77ETrNlpRof07ZtW1fSJtoXtE+Ead26tevR8wPeg8dqTYpTvGjf1T4cRq+v9fDjh3744YfQWL0vf37RsebPH8modV/bTfuyEthkx7unbRssJUwVm+ocEdZrGDzuU50jgse9tkOqc4T29+Bxn+ocETzu9bmlOkfo+Awe9/qstW2aLV5sZToHVCxMPL9C86VjJrTMsPe2bGy5LV6c/L35bez/VJeXl4Vuh2Vjy23RoqXrl+zzyMurOvcsLC+3hVFjFy60hQuTb18pKSm1gvyosSVWkF91TtO6hn1u0qxZiRUuOf9pG2hbhMc2s8KCqvRB2zbsM5bi4mZWtGTyCn1mZWXhE1hoXysqrDqvVlQsTjnZRVFRsRUvOQfrmFuwYH6k2MrKitC/RS62sChxvk4XW1hYZM0S1QOVKf9u6dhoVtwsUmxBQaGVBBohU8cWWEmzkkix+fkFVrrk74vovaVqnCgtKY0Wm5ef+FskC+YvsIrK5Bf9Okc2L22+NHbBgtAGgmVjy9x+EabJzxH5RbZ0bdPnBsqnfB6hvPS3334LjdU52O+XyXKDVMdgziX+/qDzyVBN/vFUO7vnE8SzzjrLJcZXX321bbTRRu4P2XXXXWdvvvmmHX/88fbMM88kEp+mFGxx1M/B1rHg1V+y31teYyXTWB+fSay+E0usj9UfvFSx6fYtAACyQV5llk8h06VLF9eq8fbbbyda9YI0y89FF11ku+++u5vtJxXN1a8rwHbt2tnzzz9frQZXV3Aa3KvSovPPP9+OPvroOq/7l19+6S5IdCW84YYb5lR3+/ISW59dacQSWx+x2XJsZGus246z3zObcFW1Fv+m78YnltjGis2OEhdiM/zs8ovMNj7f8tp2b/S/MV999ZVrKFfvhBq0c7rFX+UJ6v4I68bwXYFVXTGpKQFXq7/uCVBz4J264XRPgMsuu8xdZNRH4h/Woh0mkxl8iM08NpNWWWKJbYzYbDk2sjNW2zCklzDk8brHLv2XWGKzIbYqktjsiZWw+Lwm+xuT0d8cy3JqnZewmmRf2+/jUmnTpo37Hqx/D/KPp6qrBgAAAHJR1if+fjafb775JunzkyZNqhYXZVlhgxX9xYW/QAAAAACWF1mf+Pfu3dt9f/nll5d5TiOb3333XTfDQM+ePSMvSzcFSzYq+4033nDfu3evqs8CAAAAlhdZn/jvuuuuboq6sWPHuik3g7X9F1xwgRs8e9BBB1Ub+KvBwJMnT3Zf+tnTPP8q59H0ebpZVzD5f+yxx9y8/pqDe7/99mvEdwgAAAA0vKyf1Ufee+89O+6441yyv8kmm7jk/aOPPnL1/Zr157777kvMUS6annOXXXZxP7/22mvVavp1Ey8tS70Fmne7a9eubr5rzeajqUFvuummRM9Afc3qE2WUNQBgiVnjzSYMrDarDwBktXzN6nOe2ZJZfRpTJvlm1rf4S7du3VyLvKbs1E1/1PqvWXlOPfVUGz58eLWkPx1dKIwaNcqOOOIIdzMELUsXAXvvvbebGrS+kn4AAAAgm+REi3+uosUfAGqBFn8AuSafFn8AAAAAWYLEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEH4iRDz/80DbYYINqXzvssEO1mPPPP989/tlnn6Vd3u+//27dunWzvffeu9rjd911l/Xo0cO23357e/755xOPl5eX23bbbWf33HNPPb4rAAAQRWGkKADLha+//tp932KLLax169bu51atWiWef+qpp+zJJ5+MtCwl8WeddZbNmzfPVl111cTjv/zyi1133XV23nnn2fTp0+3CCy+0PfbYw/Lz8+2xxx6zRYsW2cEHH1zv7w0AAKRG4g/EyDfffOO+X3bZZda5c+fE43/88Yddc8019uijj0ZazqeffmoXX3yxffnll8s89+OPP1pFRYV16tTJmjdvbn/++afNmTPHWrZs6XoCjj76aCstLa3HdwUAAKKg1AeIYYv/E088YWeeeaZr4ZepU6e6pL937962ySabpF3OqFGjbNKkSXbiiScu89waa6zhWvd1kaGvFVdc0fUuPP3006534LDDDmuAdwYAANKhxR+IYeLva+yfe+4510K/33772Q033GB77rmnHXnkkWmX07NnT1eu06xZM7vtttuqPdeuXTs755xzbOjQoVZUVOR6FyorK+2OO+6www8/3Fq0aNFA7w4AAKRCiz8QEwsWLHADcVVvP3bsWFdvX1JS4hJ3leT07dvX8vLyIi1r5513tvXWWy/0+WOPPdbeffddGzdunO21115ugK9q/4866ii74oor3MDfQw45xGbMmFGP7xAAAKRC4g/EhJL8W265xW666SZbffXVrWvXrq7lXhcEviegIai1XxcX6iHQ69x333127rnn2syZM23w4MEN9roAAKA6En8gJsrKylxd/nfffZd4rLi42H3XTDsN5ZVXXrEffvjB9QJMmDDBPaZegE033dS++OKLBntdAABQHYk/EBPffvutS7hPP/10W7x4sf31119uXn/V4Wve/oYybNgwO+CAA2yVVVZJPKbBv/oCAACNh7+8QExsuOGGbv5+TcG5zz77uC+V2ygpb9OmTcrf1SDgU045xV599dWMXvP111935T3HHXdcYh3k888/t8mTJ9vGG29ch3cEAAAyQeIPxIQG7t58882u1X/WrFluas0jjjjC/vOf/0TqLXjttdfs+++/z+g1b731VneBoSk+RWMKNJ2nLgQKCwvttNNOq/X7AQAAmcmr1Mg7NAi1rKqcQjOmbLTRRk29OgCQG2aNN5sw0KxiYVOvCQBEk19ktvF5Zm27Wzbnm7T4AwAAADHADbyWU+PHN/UaAEBmdBuJ9dc3a9XUKwIAy6mcSfynTJliQ4YMsQ8++MBmz55tq622mrvL6AknnGArrLBC5OVMnTrVdt1115Qxb7/9trVu3dpy3aBBZgvpKQeQI5o3N7v55qZeCwBYfuVE4v/pp5+6O36qfmmzzTZz839rGkJNEzh69Gh76KGHrEWLFpGW5ecNX3/99UProJo1a2bLAyX9JP4AcgXnKwCIeeK/cOFCN++4kv6BAwdav3793OO62+gZZ5zhEv9BgwbZJZdcklHir9lMdCdRAAAAIA6yfnCv5g+fPn269erVK5H0S0lJiV155ZVuBPPIkSPd1IRR+DuHdunSpcHWGQAAAMg2WZ/4jxkzxn3v06fPMs+1atXKevTo4XoFxo0bF7nFX3cq7dy5c72vKwAAAJCtsr7UR3f9lA022CDp8506dXIXBxMnTrS+ffumXNaPP/5ov/76q/udRx991J588kk3aLi4uNi23nprO+mkk9z4gfqmWyVUVFQkfS4/f+m1V1hMprG6UZNZXrXXT6UqfnmNdf/6aEsVTiyxjRGbPcdG9sX68MrEv0t/Py94TrM0y80gNhi99HWJJbYpYxtufye2Ic8RVY/nVTunVaY8//lzYF1jM7klV9Yn/j///LP7vuqqqyZ9fpVVVnHff/nll8j1/d98841dddVVttVWW9k222xjX331lb366qv2+uuv29VXX+3ubFqfysrK3J1Pa1KZkr+jqegiJOzDKy0ttTXXXDPxf91BdfHixcvEaceoWmZp4rH58+eHLlcXE1q2p7ETYRcVWrbWuTaxZWULbPHi8IuV4MxM2l7J3luy2PLyMlu0KDxW6+Bzi7Kyclu0aFGk2PLyclu4MDxW2yw/vyq4vHyh63WKEqs4xYfHllh+fkGkWJW7FRT42EVuncNjm1lBQdXhrm2gbRFGg9t1V92q2MXu8wiPLbbCwiL3sz6zBQvCY3WBrd62qtgKt/+ExxZZUVGx+1n72Pz5UWMr3f4eRq+v9YgWW2jFxVUD/XX4aJxRGG0vPylA+tgCa9asJPH/VLH6fPU5R4vNt5KS2h332XKOMKuwyspSty7z9V4DN/BaoXkG54hqseW2eHHq497/qa46n0SN1fkk9XGfl1fVWLNQ55OosQt1Pkl1LJdaQX7U2BIrWHI+0bqmOkdonyxccj7RNtC2CI9tZoVLzifatqnOETqGipacT/SZVX3OYbHFVrTkfFJRofNJeKyO+eIl5xPtYwsWzI8UW1mp80mK2MKl54h0sTr3NVsSq6Qv7TliyfkkXazO1SWBSUbSniMink/0t6U0cD5Je9xHPZ/k1ThHzF9gFZUpjvvSDM4R1WLL3H4RpsnPEflFtnRtzWbNmmVz584NXW6HDh0SfxM1W+Vvv/0WGtu+ffvEfqkG7Dlz5lR7PtUxmHOJvz/ogn/8gvzjqXb2mol/x44d7dZbb7V11lnH/V873e2332433HCDnX/++da1a1dbe+216/FdAAAAAE0r6xN/Xc2mK4GJ2s1x6qmn2v777+9ajIPz9OvKVmU+H3/8sSsbeuSRR+ycc86x+qLWEV1spLPuuutGXqauFKN0n0vwSjydsAususYGWzjTx0afTlWtSYkGlySCm0Kt0/6KOV1sVet01NiixFV7uljF+Rby+o0tTLTSp4tVnG/9Tx9bYAUFzSPF6lgNtuCmjs2PHKvjM3psXoPE6uf6iq2poWIzOe6z5Ryht6ftl+gNCLT41/YcoePeLMVJItDlX3U+iRqr80lRpNginU+ixhbpfFJY77E6l6Q6R1SP1fmkIFKsziXNm0eNTX2OCMaqdTp6bH7kWPWsNESsfq6/2Bw/R5Q2zDlCPddRNck5Ir/6Md62bVtr06ZN+FIDfzgUl+r+UcFYjW9deeWVqz2vypVUvVM5lfgrSVf3R1g3hu8KjLKz62SWqiV/l112cYn/Z599ZvVJH1iwPj9MlJjaxNa8EIhrrKvTixxOLLGNEZsdx0a2xPrwvMS/eWnrc9MuN6PYpf8SS2w2xFZFEps9sRIWnxdal59OXWMzOddm/aw+7dq1c99nzpyZ9Hlf2+/j6mL11Vd336NeNQEAAAC5IusTfz+bjwbkJjNp0qRqcalo4O5pp53mZgBKZsaMGdUuAAAAAIDlRdYn/r1793bfX3755WWe08jmd99919Vy9ezZM+2yPv/8c7ec559/PunzzzzzjPu+ww471Hm9AQAAgGyS9Yn/rrvu6qaxHDt2rBt0G6ztv+CCC9xsPgcddFC1QRGa4mzy5MnuKzjN4qGHHuq+33333fb2228nHteUT9dcc42NHz/ezfSzzz77NNr7AwAAABpD1g/u1Yhvlegcd9xxNmDAABsxYoSttdZa9tFHH7n6/i5dutgZZ5yxzNz//mZer732mouXPffc095//3174IEH7JhjjrHNNtvM3R9APQHTp0939wQYOnRomlkdAAAAgNyT9S3+0q1bN3vsscds9913d3ffVet/ixYt3PScw4cPr3ZDp3QuuugiGzx4sHXv3t31CIwePdpNMaYLAZX6rLfeeg36XgAAAICmkPUt/l7nzp1dwh6FWvjDBvCKLiD0BQAAAMRFTrT4AwAAAKgbEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZqnfi/8MILtnDhwvpdGwAAAAANorC2v3jGGWfYyiuvbHvvvbf169fPNtlkk/pdMwAAAABN3+LfoUMH++233+yBBx6wAw44wPbdd18bPny4zZkzp/7WDgAAAEDTJv4vvfSSPfzww3bQQQdZixYtbOLEiTZw4EDbYYcd7LTTTrMxY8ZYRUVF/awlAAAAgKYp9ZEtttjCfV144YX26quv2lNPPWVvvfWWvfLKK+7/bdq0sX322cf69+9v66+/ft3WFAAAAEDTJP5ecXGx9e3b133NmjXLnn32WZf8f/zxx3bPPfe4ry5dutj+++/vxgSsuOKK9fGyAAAAAJpqOs+2bdva0UcfbUOHDrWzzz7bSktLrbKy0j777DO79NJLbfvtt7crrrjC5s6dW98vDQAAAKAhW/y9v/76y9X+jxo1ysaPH2+LFy92SX/r1q1dS/+MGTNs7NixbkCw4u677z5bZ5116nMVAAAAADRE4q/Efty4ca6+f/To0bZgwQL3WEFBge24446uvGennXaywsKql5o5c6Yb/KsyoMsvv9zuuuuuuq4CAAAAgIZK/L/88kt7+umnXT3/7NmzXbLvp/lUsr/ffvtZu3btlvm9VVZZxS655BL3/IcffljblwcAAADQGIm/btqVl5fnEn7V8e+xxx4u4d96663T/m7z5s3d9xVWWKG2Lw8AAACgsUp9Nt98c5fsazYfn8xHUVJSYoMGDbKOHTvW5eUBAAAANHTi/8ILL9i6665bq99VCdBee+1V25cGAAAA0FjTefqk/+2337azzjrLzeAT9J///MeOPPJIe/PNN2v7EgAAAACyYR7/G2+80Y499lh7/vnnbdq0adWe++6779yUnieccILdcsstdV1PAAAAAE2R+Gse/mHDhrkBvgceeKC1aNFimRb/o446yvLz823IkCH2zjvv1GU9AQAAADRFjf/DDz/skn7dhVcz/NTUpUsX97Xhhhva+eefb/fee69ts802dVlXAAAAAI3d4j9hwgRbbbXVkib9QXq+bdu27oZddTFlyhQ7++yz3c3Aunbtan369LEbbrjB/vzzT6urq6++2jbYYAO7+eab67wsAAAAYLlK/HWH3jZt2kSK1QVCXRL0Tz/91Pr372+jRo1yNwDr3bu3/fXXX67U6OCDD7bff/+91st+66237J577qn17wMAAADLdeK/6qqrugG85eXlKeM028/UqVOtdevWtXqdhQsX2umnn+4S/YEDB9qIESNs8ODB9uqrr9rOO+9sX3/9tbsnQG3MmTPHzj333MRdhwEAAIDlVa0Tf9XrqxVfA3dTufPOO23u3LnWrVu3Wr3Oc889Z9OnT7devXpVKyvSTcCuvPJKd+OwkSNH2rx58zJetgYg//rrr7blllvWat0AAACA5T7xP+yww6ygoMBuv/12N4//u+++6xJ8tdD/9ttv9t5779l5553npvxU3NFHH12r1xkzZoz7rpr+mlq1amU9evRwrzlu3LiMlvvggw+6Zf/zn/90g5ABAACA5VmtZ/Xxs/VoVh/N46+vmlRCo5l/dAFQ2+RapTyiwbfJdOrUySXwEydOtL59+0Za5jfffOMG9Kql/8QTT3QlRA1J26GioiLpc5ru1AuLyTRW29wsr9rrp1IVv7zGun99tKUKJ5bYxojNnmMj+2J9eGXi36W/nxc8p1ma5WYQG4xe+rrEEtuUsQ23vxPbkOeIqsfzqp3TKlOe//w5sK6xmZSs1zrx963+G220kQ0dOtS1+Kvl3VMrvxLrU045xXr27Fnr1/j5558TYwqS0WBf+eWXXyItr6yszM4880wrKiqya6+91q1nQ9Nrfvvtt8s8rjKlNdZYo9rMRWEfXmlpqa255pqJ/3///ffL3C1ZtGNULbM08dj8+fNDl6uLCS07OGg77KJCy9Y61ya2rGyBLV4cfrGywgorBGLLkr63ZLHl5WW2aFF4rNbB5xZlZeW2aNGiSLEau7JwYXistll+flVwefnCavt+qljFKT48tsTy8wsixarcze+/WtdU421KSppZQUHV4a5toG0RplmzZlZY6GMXu88jPLbYCguL3M/6zBYsCI8tLi52x11VbIXbf8Jji6yoqNj9rH1s/vyosZVufw+j19d6RIsttOLiZu5nHT4aZxRG20vbLVpsgTVrVpL4f6pYfb76nKPF5ltJSe2O+2w5R5hVWGVlqVuX+XqvFUv3/xWaZ3COqBZbbosXpz7u/Z/qqvNJ1FidT1If93l5VY01C3U+iRq7UOeTVMdyqRXkR40tsYIl5xOta6pzhPbJwiXnE20DbYvw2GZWuOR8om2b6hyhY6hoyflEn1nV5xwWW2xFS84nFRU6n4TH6pgvXnI+0T62YMH8SLGVlTqfpIgtXHqOSBerc1+zJbFK+tKeI5acT9LF6lxdsuR8EukcEfF8or8tpYHzSdrjPur5JK/GOWL+AquoTHHcl2ZwjqgWW+b2izBNfo7IL7Kla2s2a9YsVwkTpkOHDom/ibNnz3bVMmHat2+f2C9Voq4xqkGpjsF6TfxFyb3q+PXh/fjjj27FtZHWXnvtaglabfmDLvjHL8g/nmpnD7rmmmtcL4Ja/Ndaa606rx8AAACQC+qc+AcT8I4dO1p909VsuhKYqN0cY8eOtQceeMCVBO23337WWNQ6EmXbrLvuupGXqSvFKN3nErwSTyfsAquuscEWzvSxS1s60lFrUqLBJYngplDrtL9iThdb1TodNbYocdWeLlZxvoW8fmMLE6306WIV51v/08cWWEFB80ixOlaDLbipY/Mjx6rlKXpsXoPE6uf6iq2poWIzOe6z5Ryht6ftl+gNCLT41/YcoePeLMVJItDlX3U+iRqr80lRpNginU+ixhbpfFJY77E6l6Q6R1SP1fmkIFKsziXNm0eNTX2OCMaqdTp6bH7kWPWsNESsfq6/2Bw/R5Q2zDlCPddRNck5Ir/6Ma57WKWa9j6Yqyku1eyXwViNb1155ZWrPf/VV1+l7J1qkMRfaibo6g5RT8BPP/3k6vBVT58p9RqoFyGsG8N3Babb2WfOnOnGJKy++up26aWXWmPSBxaszw8TJaY2sTUvBOIa6+r0IocTS2xjxGbHsZEtsT48L/FvXtr63LTLzSh26b/EEpsNsVWRxGZPrITF54XW5adT19hMzrV1SvzfeOMNd7dblc6km89fapP4t2vXziX+StyVtNfka/sVl8qtt97qaqI0JuGyyy6r9twXX3zhvr/88suudn699dazk08+OeN1BQAAALJVrRP/zz//3A3c1eCJdGU26jbcYostavU6ms1HFxaaiadr167LPD9p0qREXCp+DMCXX37pvpLR6+ire/fuJP4AAABYrtR6Hv/77rvPlfKsv/76bqDsTTfd5B7ffffd7e6773Y319p6663dY/p+//331+p1evfunWiNr0kjmzWbkGq50s0cpCk7NeVnsq8jjzzSxZx66qnu/7VdVwAAAGC5S/zff/99N1Bn8ODBtu+++7qEX6U4U6dOtW233db69+/vEujddtvNJee6A29t7Lrrrm4aSw3MfeSRR6rV9l9wwQWuJf+ggw6qNihCU5xNnjzZfaWaZhEAAACIi1on/ppzVPPFB2ei0U29gvX+GmygAbXyxBNP1Op1NOJbPQr6PmDAAHdB8a9//ctdULz22mvuxmBnnHHGMnP/a+Yeffn7AAAAAABxVuvEX2pOJ7TOOuu4mn/diMrTxYGmnlQJTW1169bNHnvsMderoHsFqPW/RYsWrjRn+PDh9XK/AAAAAGB5VuvBvZpzVDPtBPkbYmnAbXCwrRLzadOm1WU9rXPnzq6sKAqtRyYXGioZ0hcAAACwvKp1i79KbFRG8+abbyYe002qNMPPe++9V60WX1NktmzZsu5rCwAAAKBxE38N6FWSr3r7a6+91s3ws/nmm7ta/JEjR9pTTz3l6v0vvPBC++OPPzK6Ky0AAACALEn8d9llF9trr73cLYJVZ68ZfnRL50MPPdRdBGhQry4ONJuPBvn6KTMBAAAANL463bl30KBB1qtXL3vrrbcStws+88wz3fz6avFXj4AuCI455hjr06dPfa0zAAAAgMZK/OfNm2crrbSSm15TX4kFFhbaVVdd5abY1Aw87du3rzbHPgAAAIAcSvyPPvpoV88/dOjQZab1lHbt2rkvAAAAADmc+GuufrXkJ0v6AQAAACwng3uLioqsefPm9bs2AAAAALIr8d9nn33cjbpefPHF+l0jAAAAANlT6nPggQfahAkT3CDeBx980LbccktX09+sWbPQ3znggANq+3IAAAAAmiLx32+//dx3Tdn5/vvvu690SPwBAACAHEv8V1999fpdEwAAAADZl/iPHj26ftcEAAAAQPYN7gUAAACQO0j8AQAAgBiodanPkUcemVF8Xl6eDR8+vLYvBwAAAKApEv/x48dHSvb9zD/+ZwAAAAA5lPifeuqpoc/99ddf9ssvv9jbb79tc+bMsZNPPtm6d+9e25cCAAAAkI2Jf/AC4LTTTrN7773X9t1339q+FAAAAIBsHtzbvHlzu+qqq2zhwoU2ZMiQhnwpAAAAAE05q0+7du1s/fXXd2U/AAAAAJbj6TxV8jNv3rzGeCkAAAAATZH4v/LKK/bDDz/Y6quv3tAvBQAAAKC+B/fedNNNoc9p+s7y8nL79ttvbdy4cW4qzz59+tT2pQAAAAA0VeJ/6623pp2bXxcA0rFjRzvhhBNq+1IAAAAAmirx79atW+oFFxZaq1atbKuttrJ+/fq5GX4AAAAA5Fjif//999fvmgAAAADI7sG9f/zxxzKPffbZZzZt2rT6WDwAAACApkz8lfCfffbZtt122y2T/A8bNswN6D3zzDOZyhMAAADI1cRfif4hhxxizz77rC1YsMCmTp1a7fnFixdbRUWFvfDCC3bMMce4u/cCAAAAyLHE/6677rJvvvnGOnToYA8//LBttNFGy7T4P/XUU7beeuvZhAkTGBMAAAAA5GLirxtzaeaeO++807bYYoukMRtuuKENHjzY8vPzbdSoUXVZTwAAAABNkfirtEfz86+99top4xTTvn17mzJlSm1fCgAAAEBTJf7FxcWJG3RFiU13sy8AAAAAWZj4qxV/8uTJywzqrennn3+2SZMmpe0ZAAAAAJCFif8ee+zhZu0566yzbM6cOUlj5s6d655X3G677VaX9QQAAADQFHfu1VSeI0aMcDfq0nz9u+66qxvM27x5c/vzzz/t66+/ttGjR7vkf4011rCjjz66LusJAAAAoCkS/xVXXNFN2Xn66ae7aT2ffvpp9xWkMQCa7nPo0KHWokWLuqwnAAAAgKZI/EVz9D/xxBNuas8xY8bYDz/8YL/99puVlpbaOuusYzvuuKPttddebnAvAAAAgBxN/KWoqMj69u3rvgAAAAAsZ4N7vW+//dZuuOGGZR6/7rrr7Pzzz3e1/gAAAAByOPF/7LHHbJ999rHbb7/dpk2bVu25Tz75xJ588knr37+/Pf7443VdTwAAAABNkfi//fbbdtFFF9miRYts2223dSU/Qcccc4yb6UfPDxgwwL744ou6rCcAAACApkj8hw8f7u7Ge8YZZ9hdd91lq666arXnd955Z7vlllvszDPPdMm/YgAAAADkWOKvUp42bdrYCSeckDLuuOOOs5YtW9r48eNr+1IAAAAAmirx/+OPP2z11Vd3rf4pXyA/39Zee203zScAAACAHEv8V1llFZs6daq7SVc6P/30k6288sq1fSkAAAAATZX4b7HFFjZ37lx78MEHU8aNHDnSZs2aZZtvvnltXwoAAABAUyX+hxxyiPs+cOBAu/7665eZzvPHH3+0m2++2S699FJXDnTYYYfVdV0BAAAANPade7feems7/vjj3Rz+d9xxh/tq3ry5+/rrr7/cl6gUSFN79uzZs7YvBQAAAKCpEn/RVJ0bbLCBm7ZzypQp9ueff7ovb80117RTTjnF9t9//7quJwAAAICmSvxlr732cl8//PCDff/99272ntLSUlt33XVtvfXWczE///yzPfroo/avf/2r1q+jC4shQ4bYBx98YLNnz7bVVlvN9txzTzed6AorrJDRsp5//nk3NmHChAlWUVFh7du3t759+9rRRx/t1h0AAABY3tQ58feUPOsr6PXXX7dHHnnE3nzzTVu8eHGtE/9PP/3UjjrqKFc+tNlmm9mmm25qH374oQ0bNsxGjx5tDz30kLVo0SLSsm666SYbOnSoFRQU2FZbbWUrrriiW/6NN95ozz77rLsgYAYiAAAALG/qLfH35syZ42byGTFihE2fPj1R559uvv8wCxcutNNPP90l/RpI3K9fP/f4ggUL3F2DlfgPGjTILrnkkrTLev/9913Sv9JKK9n9999vG264oXtcy9ZFiS5QdGEwYMCAWq0rAAAAsNzN6lPTu+++6xLxHXfc0W644QY3y48S/pKSEjvooIPsySefrNVyn3vuOXcB0atXr0TSL1rulVde6QYT60Jj3rx5aZfl1+Ef//hHIukXLcP3Rrzxxhu1Wk8AAABguW3x//333+2JJ55w9fuqwRd/Q69OnTrZwQcfbPvuu68rp6mtMWPGuO99+vRZ5rlWrVpZjx49XMy4ceNcnX4qmlpUSX+bNm2WeU6lSFJYWO+dIAAAAECTq1WWq5r4hx9+2F544QUrKytLJPt+Ks9VV13VRo0aVS8r+PXXX7vvmj0oGV1gKPGfOHFi2sRfSX3Hjh2T3ln46quvdj83xAxE2j4aRJxMfv7STpewmExjq8qqlpZWpbu7crAMa/mLdf/6aEsVTiyxjRGbPcdG9sX68MrEv0t/Py94TrM0y80gNhi99HWJJbYpYxtufye2Ic8RVY/nVTunVaY8//lzYF1j051ra5X4K6FXMq/W/S+//DLxQhoku+2229o+++xju+66q7ujb23r+ZPRjECii4lkVlllFff9l19+yXjZ11xzjX388cfuS+t83HHHuXsT1DddHH377bfLPK4LpTXWWCPxf/WahH14mm1I06N6mkHJ91IE6X1ULXPp7ETz588PXa4uJoIzGWnsRNhFhZatda5NbFnZAlu8OPxiJTgzk7ZXsveWLLa8vMwWLQqP1Tr43bGsrNwWLVoUKba8vNwWLgyP1TbLz68KLi9f6MaiRIlVnOLDY0ssP78gUqzK3XT8VcUucuscHtvMCgqqDndtA22LMM2aNUv0fGnb6vMIjy22wsIi97M+swULwmOLi4utqMjHVrj9Jzy2yIqKit3P2sfmz48aW+n29zB6fa1HtNhCKy5u5n7W4ePvTZKMtpe2W7TYAmvWrCTx/1Sx+nz1OUeLzbeSktod99lyjjCrsMrKUrcu8/VeK5bu/ys0z+AcUS223BYvTn3c+z/VVeeTqLE6n6Q+7vPyqhprFup8EjV2oc4nqY7lUivIjxpbYgVLzida11TnCO2ThUvOJ9oG2hbhsc2scMn5RNs21TlCx1DRkvOJPrOqzzksttiKlpxPKip0PgmP1TFfvOR8on1swYL5kWIrK3U+SRFbuPQckS5W575mS2KV9KU9Ryw5n6SL1bm6ZMn5JNI5IuL5RH9bSgPnk7THfdTzSV6Nc8T8BVZRmeK4L83gHFEttsztF2Ga/ByRX2RL19Zs1qxZNnfu3NDldujQIfE3UbNValbMMJo8x++Xv/76qxtPG5TqGKxV4q+Bs0r6tUP5D75r16629957u6/WrVtbQ/EHXfCPX5B/PNXOHubxxx9PbGhtUF08aOO3bdu2TusMAAAAZJtIib+m5NSVl6bS3Hnnnd38+WuvvXbDr92Sq9l0JTCZdnN4Tz31lBsnoHIizQz0zDPPuDKmp59+OvRCozbUOpKsxKgm3fsgKl0phqnZ45LJvQkyed+ZxAZbONPHLm3pSEetSYkGlySCm0Kt0/6KOV1sVet01NiixFV7uljF+Rby+o0tTDk+JRirON/6nz62wAoKmkeK1bEabMFNHZsfOVYtT9Fj8xokVj/XV2xNDRWbyXGfLecIvT1tv0RvQKDFv7bnCB33ZilOEoEu/6rzSdRYnU+KIsUW6XwSNbZI55PCeo/VuST1GLZgrM4nBZFidS5p3jxqbOpzRDBWrdPRY/Mjx6pnpSFi9XP9xeb4OaK0Yc4R6rmOqknOEfnVj3E1IicbU5pYauAPh+JSNaIHY5W31px2/quvvkrZO1XrGv/vvvvOPvroI2vZsqW7APBlNg1JZR1qlQ/rxvBdgZns7N7qq6+e6L244447XH2/LgLUE3DYYYdZfdEHFqzPDxMlpjaxmZReLc+xrk4vcjixxDZGbHYcG9kS68PzEv/mpa3PTbvcjGKX/ksssdkQWxVJbPbESlh8Xmhdfjp1jc3kXBspe7zuuuusZ8+ebspMDaRV6U/v3r3tmGOOca3mf/75pzWUdu3aue8zZ85M+ryv7fdxtaUrOPVkyOeff16nZQEAAADZJlLirzr+u+++21577TU79dRT3SBTDZp4++237fzzz7ftttvOzjrrLBs7dmy9r6Cfzeebb75J+vykSZOqxaWim3Npvn7N4pOM775JNbgLAAAAWO5v4KXSGCX+r776qt17773ugkB1VKorev755+3kk092cfr/hAkT6mUF1bMgL7/88jLPaWSzbhymdVCPRDrvvPOOvfTSS+6mYMm8/vrr7vumm25a5/UGAAAAlos7926zzTauBEg3zlLpj5JlP8BWJUGql99vv/3s/vvvTzlFUTqaIlQ9DOpN0CDjYG3/BRdc4Gbz0Z2Bg4MiNMXZ5MmT3VdwmsVDDz3Ufb/lllvcIN5gvN7L+PHj3biF/v3713p9AQAAgGyUV1mb6XBCqOxm5MiRbupPTYvpXiAvz808oMHAN954Y62W+95777k59pXsb7LJJrbWWmu5Qcaq7+/SpYvdd9991eZ2nzZtmu2yyy7uZ5UnKd67+OKL3b0INDhW9xxYaaWV3H0JVP6ji4fbbrvNDfatD1quLkw08HijjTayxjR+vNnAgbqoadSXBYBa0xwNw4aZtVo83mzCwNBZfQAg6+QXmW18nlnb7o3+0pnkm7Vu8U9m/fXXt/POO8+VzKhVfaeddnLTd+mmISqxqa1u3brZY489Zrvvvrv9+OOPrvW/RYsWruxo+PDh1ZL+dC677DJ3AbL11lu76Y/UY6FSoaOOOspN41lfST8AAACQTQobZKGFha5ER1+6c9mTTz7pvuqic+fONnjw4EixauGfOHFi6POavcfP4AMAAADEQb22+CejGxgcf/zxbvAvAAAAgOU08QcAAADQ9Ej8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgotBwxZcoUGzJkiH3wwQc2e/ZsW2211WzPPfe0E044wVZYYYWMljV27Fh74IEH7PPPP7c//vjDWrZsaVtttZUdd9xx1rVr1wZ7DwAAAEBTyYkW/08//dT69+9vo0aNslVWWcV69+5tf/31lw0bNswOPvhg+/333yMv6/rrr7cTTzzRxo0bZ2uuuabtuOOOttJKK9lLL71khxxyiD311FMN+l4AAACAppD1Lf4LFy60008/3SX6AwcOtH79+rnHFyxYYGeccYaNHj3aBg0aZJdccknaZb3//vt22223WfPmze2OO+6wrbfeOvHcI488YgMGDLCLL77YttlmG9ejAAAAACwvsr7F/7nnnrPp06dbr169Ekm/lJSU2JVXXumS+JEjR9q8efPSLktxopKeYNIv6jlQ639ZWZlr/QcAAACWJ1mf+I8ZM8Z979OnzzLPtWrVynr06OF6BVS6k44uFjp37ux+J5mOHTu677/88kud1xsAAADIJllf6vP111+77xtssEHS5zt16uQuDiZOnGh9+/ZNuax05UCffPKJ+7766qtbfaqsrLSKioqkz+XnL732CovJNDYvL0//Vnv9VKril9dY96+PtlThxBLbGLHZc2xkX6wPr0z8u/T384LnNEuz3Axig9FLX5dYYpsytuH2d2Ib8hxR9XhetXNaZcrznz8H1jU23bk2pxL/n3/+2X1fddVVkz6vwb710UqvsQIffvihFRUV2a677mr1SeVD33777TKPq0xpjTXWqDZzUdiHV1pa6gYje99//70tXrx4mTjtGFXLLE08Nn/+/NDl6mJCy/Y0diLsokLL1jrXJrasbIEtXhx+sRKcmUnbK9l7SxZbXl5mixaFx2odfG5RVlZuixYtihRbXl5uCxeGx2qb5edXBZeXL3S9TlFiFaf48NgSy88viBSrHqyCAh+7yK1zeGwzKyioOty1DbQtwjRr1swKC33sYvd5hMcWW2FhkftZn9mCBeGxxcXF7viqiq1w+094bJEVFRW7n7WPzZ8fNbbS7e9h9Ppaj2ixhVZc3Mz9rMNH44zCaHtpu0WLLbBmzUoS/08Vq89Xn3O02HwrKandcZ8t5wizCqusLHXrMl/vtWLp/r9C8wzOEdViy23x4tTHvf9TXXU+iRqr80nq4z4vr6qxZqHOJ1FjF+p8kupYLrWC/KixJVaw5HyidU11jtA+WbjkfKJtoG0RHtvMCpecT7RtU50jdAwVLTmf6DOr+pzDYoutaMn5pKJC55PwWB3zxUvOJ9rHFiyYHym2slLnkxSxhUvPEelide5rtiRWSV/ac8SS80m6WJ2rS5acTyKdIyKeT/S3pTRwPkl73Ec9n+TVOEfMX2AVlSmO+9IMzhHVYsvcfhGmyc8R+UW2dG3NZs2aZXPnzg1dbocOHRJ/EzVb5W+//RYa2759+8R++euvv9qcOXOqPZ/qGFxmNS3L+YMu+McvyD+eamdPR70F559/fqL+n4G9AAAAWN5kfYu/rmbTlcBk2s1Rc6pQ3QtAV1o77bSTnXbaaVbf1Drixw+ksu6660Zepq4Uo3SfS/BKPJ2wC6y6xgZbONPHLm3pSEetSYkGlySCm0Kt0/6KOV1sVet01NiixFV7uljF+Rby+o0tTLTSp4tVnG/9Tx9bYAUFzSPF6lgNtuCmjs2PHKuWp+ixeQ0Sq5/rK7amhorN5LjPlnOE3p62X6I3INDiX9tzhI57sxQniUCXf9X5JGqszidFkWKLdD6JGluk80lhvcfqXJLqHFE9VueTgkixOpc0bx41NvU5Ihir1unosfmRY9Wz0hCx+rn+YnP8HFHaMOcI9VxH1STniPzqx3jbtm2tTZs24UsN/OFQXOvWrSPFanzryiuvXO35r776KmXvVE4l/irrUFIe1o3huwIz2dm9F1980c477zy3sTR4WNOC+tKJ+qQPLFifHyZKTG1ia14IxDXW1elFDieW2MaIzY5jI1tifXhe4t+8tPW5aZebUezSf4klNhtiqyKJzZ5YCYvPC63LT6eusZmca7O+1Kddu3bu+8yZM5M+72v7fVxUuguw7g+gpP/www+3m266KU1LDwAAAJC7sj7x97P5fPPNN0mfnzRpUrW4dFQ2pFb+wYMHu1bzCy64wC666KKMWtABAACAXJP12W7v3r3d95dffnmZ5zSy+d1333W1XD179oy0vAsvvNCefPJJV6+mVv8jjzyy3tcZAAAAyDZZn/hrak1NYzl27Fh75JFHqtX2q7Ves/kcdNBB1QZFaIqzyZMnu6/gNItPPfWUPf74466O/9Zbb3WDeQEAAIA4yPrBvRrxffXVV7tpNgcMGGAjRoywtdZayz766CNX39+lSxc744wzlpn739/M67XXXnPxms/1xhtvTIy01gWAvpLZfvvtbd99922EdwcAAAA0jqxP/KVbt2722GOP2S233GLjx493df1K5tXSf8wxx1S7oVOqufpnzJiRuDAYNWpUaKymSiLxBwAAwPIkJxJ/6dy5sxuQG4UuCpToB2288cbLPAYAAADERdbX+AMAAACoOxJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZyJvGfMmWKnX322bbTTjtZ165drU+fPnbDDTfYn3/+Wafl/vrrr7bddtvZKaecUm/rCgAAAGSbnEj8P/30U+vfv7+NGjXKVlllFevdu7f99ddfNmzYMDv44IPt999/r9VytYzTTjvNZs6cWe/rDAAAAGSTrE/8Fy5caKeffrpL0gcOHGgjRoywwYMH26uvvmo777yzff311zZo0KCMlzt16lQ78sgj7b333muQ9QYAAACySdYn/s8995xNnz7devXqZf369Us8XlJSYldeeaU1b97cRo4cafPmzYu0vAULFtjtt99u++23n3322We29tprN+DaAwAAANkh6xP/MWPGuO+q6a+pVatW1qNHD9crMG7cuEjLe/75510PQWFhoV1xxRXU9gMAACAWCi3LqZRHNthgg6TPd+rUyV0cTJw40fr27Zt2eSuvvLJL9o855hhbaaWV7IknnrCGVllZaRUVFUmfy89feu0VFpNpbF5env6t9vqpVMUvr7HuXx9tqcKJJbYxYrPn2Mi+WB9emfh36e/nBc9plma5GcQGo5e+LrHENmVsw+3vxDbkOaLq8bxq57TKlOc/fw6sa2y6c21OJf4///yz+77qqqsmfV6DfeWXX36JtDyNC9BXYyorK7Nvv/12mcdVprTGGmtUm7ko7MMrLS21NddcM/H/77//3hYvXrxMnHaMqmWWJh6bP39+6HJ1MaFlB0uhwi4qtGytc21iy8oW2OLF4RcrK6ywQiC2LOl7SxZbXl5mixaFx2odfG5RVlZuixYtihRbXl5uCxeGx2qb5edXBZeXL3S9TlFiFaf48NgSy88viBSrcreCAh+7yK1zeGwzKyioOty1DbQtwjRr1sz1iFXFLnafR3hssRUWFrmf9ZktWBAeW1xcbEVFPrbC7T/hsUVWVFTsftY+Nn9+1NhKt7+H0etrPaLFFlpxcTP3sw4fjTMKo+2l7RYttsCaNStJ/D9VrD5ffc7RYvOtpKR2x322nCPMKqyystSty3y914ql+/8KzTM4R1SLLbfFi1Mf9/5PddX5JGqsziepj/u8vKrGmoU6n0SNXajzSapjudQK8qPGlljBkvOJ1jXVOUL7ZOGS84m2gbZFeGwzK1xyPtG2TXWO0DFUtOR8os+s6nMOiy22oiXnk4oKnU/CY3XMFy85n2gfW7BgfqTYykqdT1LEFi49R6SL1bmv2ZJYJX1pzxFLzifpYnWuLllyPol0joh4PtHfltLA+STtcR/1fJJX4xwxf4FVVKY47kszOEdUiy1z+0WYJj9H5BfZ0rU1mzVrls2dOzd0uR06dEj8TZw9e7b99ttvobHt27dP7JeajXLOnDnVnk91DC6zmpbl/EEX/OMX5B9PtbMDAAAAcZf1Lf66mk1XApNpN0djU+tIx44d08atu+66kZepK8Uo3ecSvBJPJ+wCq66xwRbO9LFLWzrSUWtSosElieCmUOu0v2JOF1vVOh01tihx1Z4uVnG+hbx+YwsTrfTpYhXnW//TxxZYQUHzSLE6VoMtuKlj8yPHquUpemxeg8Tq5/qKramhYjM57rPlHKG3p+2X6A0ItPjX9hyh494sxUki0OVfdT6JGqvzSVGk2CKdT6LGFul8UljvsTqXpDpHVI/V+aQgUqzOJc2bR41NfY4Ixqp1OnpsfuRY9aw0RKx+rr/YHD9HlDbMOUI911E1yTkiv/ox3rZtW2vTpk34UgN/OBTXunXrSLEa36qy9aCvvvoqZe9UTiX+KutQ90dYN4bvCsxkZ29s+sCC9flhosTUJrbmhUBcY12dXuRwYoltjNjsODayJdaH5yX+zUtbn5t2uRnFLv2XWGKzIbYqktjsiZWw+LzQuvx06hqbybk260t92rVr576H3WTL1/b7OAAAAAA5mPj72Xy++eabpM9PmjSpWhwAAACAHEz8e/fu7b6//PLLyzynkc3vvvuuq+Xq2bNnE6wdAAAAkBuyPvHfdddd3TSWY8eOtUceeaRabf8FF1zgZvM56KCDqg2K0BRnkydPdl+pplkEAAAA4iLrB/dqxPfVV19txx13nA0YMMBGjBhha621ln300Ueuvr9Lly52xhlnLDP3v7+Z12uvvebiAQAAgDjL+hZ/6datmz322GO2++67248//uha/1u0aGGnnnqqDR8+vNoNnQAAAADkYIu/17lzZxs8eHCkWLXwT5w4MVJs//793RcAAACwPMuJFn8AAAAAdUPiDwAAAMQAiT8AAAAQAyT+AAAAQAyQ+AMAAAAxQOIPAAAAxACJPwAAABADJP4AAABADJD4AwAAADFA4g8AAADEAIk/AAAAEAMk/gAAAEAMkPgDAAAAMUDiDwAAAMQAiT8AAAAQAyT+AAAAQAyQ+AMAAAAxQOIPAAAAxACJPwAAABADJP4AAABADJD4AwAAADFA4g8AAADEAIk/AAAAEAMk/gAAAEAMkPgDAAAAMUDiDwAAAMQAiT8AAAAQAyT+AAAAQAyQ+AMAAAAxQOIPAAAAxACJPwAAABADJP4AAABADJD4AwAAADFA4g8AAADEAIk/AAAAEAMk/gAAAEAMkPgDAAAAMUDiDwAAAMQAiT8AAAAQAyT+AAAAQAyQ+AMAAAAxQOIPAAAAxACJPwAAABADJP4AAABADJD4AwAAADFA4g8AAADEAIk/AAAAEAMk/gAAAEAMkPgDAAAAMUDiDwAAAMQAiT8AAAAQA4WWI6ZMmWJDhgyxDz74wGbPnm2rrbaa7bnnnnbCCSfYCiuskNGyfv75Zxs6dKj973//s59++snatm1rO++8s/3zn/+01q1bN9h7AAAAAJpKTrT4f/rpp9a/f38bNWqUrbLKKta7d2/766+/bNiwYXbwwQfb77//HnlZP/zwg+2///72yCOPWElJie20005WUFBgDzzwgO233342Y8aMBn0vAAAAQFPI+sR/4cKFdvrpp7tEf+DAgTZixAgbPHiwvfrqq66V/uuvv7ZBgwZFXt65555rM2fOtNNOO81dSGhZL730kruAUE/AxRdf3KDvBwAAAGgKWZ/4P/fcczZ9+nTr1auX9evXL/G4WuuvvPJKa968uY0cOdLmzZuXdlnvvfeeffjhh9axY0c75ZRTEo+rxf/CCy+0NdZYw9544w2bNGlSg70fAAAAoClkfeI/ZswY971Pnz7LPNeqVSvr0aOH6xUYN25c5GXtuuuulp9f/a0XFRXZLrvs4n4ePXp0Pa09AAAAkB2yfnCvSnlkgw02SPp8p06dXEI/ceJE69u3b52Wtf7667vvWlZ9qqystIqKiqTPBS9AwmIyjc3Ly9O/VlS09PVTqYpfXmPdvz7aUoUTS2xjxGbPsZF9sYVL/iK538orNMtf+vt5ie3rtnDq5WYQG4yuikwVTyyxjRHbcPs7sQ14jtA5K/iXoFJ/CypTnv/8ObCusenOtTmV+KvuXlZdddWkz2uwr/zyyy/1tiyNAagPZWVl7vv8+fPtk08+SZrIFxcXV4sP+/CixmrHUO+F4s85J7v+qDdEbC4kesQSGx6bHcdRtsTqv9Ommc2ozDdreU49Ldf9myo48Ic6xrFKbvKyKHbpL8Q6NpuOz+U31urvOJqRZ3kzv3Q/qhpl8eLFoaHNmjVLrMeiRYvcVxjlf77xN1msbwz2eWdOJ/5Kmn1NfzL+cQ3+bcxlRRFslU/WQq/HUn3QtY3VjhY8aVTLk9PItdhsWQ9iiSW2fmLLy93/ql0cJYmOvuCM44kllth4xdbzssuj5ZA+J41iwYIFkeLSVY7kROKvgbdR3kiUbg4tK4oorxeFWt51xaerNF3ZAQAAAPVJLf3KXZV35nzir5tz/fbbb6HdF/4qSLP7RFmWpFtWpjcEC7PpppvWy3IAAACA5X5Wn3bt2qWsu/e1/T4uyrLCxgNksiwAAAAgl2R94u9n4Pnmm2+SPu/n3A+bqSfZssLm6c9kWQAAAEAuyfrEv3fv3u77yy+/vMxzv/76q7377ruufr5nz56Rl/XKK68sMyZAtfivvfZatTgAAABgeZH1ib9utrXmmmva2LFj7ZFHHqlWj3/BBRe4GXgOOugga926dbUkfvLkye5LP3tbbLGFde3a1c3nf+ONNyaSf82Cc8UVV9iMGTNsp512ss6dOzfyuwQAAAAaVl5lJrP+N5H33nvPjjvuOJfsb7LJJrbWWmvZRx995Gryu3TpYvfdd1+1AbnTpk1L3IVXrfiK93QxcNhhh7nego4dO7obgH355Zf2ww8/uLiHH36YGn/EQvA4SUazA6y44oq2zjrruF6www8/3P0/G6in78gjj3Q/f/HFF1a45M5PN998s91yyy225ZZbumO5rjTd2uzZs6udQ5544gk7//zz3f1A3njjjTq/BlKbM2eOPf300/bmm2+6RhtN9qB9s02bNrbZZpu5xqHdd999mbuxp9pPauuII46w8ePH20knnWRnnHGGNTRfdnrPPffYtttum9Exvd9++9nVV1+dMj64ferzxpX6jNTo5u+Nkwvqe18BslXWt/hLt27d7LHHHnMn9x9//NG1/rdo0cJOPfVUGz58eEaz8Ky33nr2+OOPW//+/e333393d/3VDRR0wI8YMYKkH7GkXi4ly8GvDTfc0M2WpYvsG264wf72t7/Z999/b3ExatQod855++23m3pVYkltUnfeeaftvPPONnDgQHvrrbdc76zusN6hQweXXD777LN2+umn27777hurfTOKp556yv19a2z33nuv9enTJ3RcHoCmVZhLicngwYMjxap1LlXrhUqHrrrqqnpcOyC3XXjhhdajR4/QlrBTTjnFXXSfe+651Uruso168/r27WulpaV1XpYudvzdvoN2220319IcZb5k1N5FF13kGnxkr732shNOOMFdjHq6oaF6Aa655hrXE3DooYfaM88843oCsHQbPvfcc9ayZctGe03+tgLZLSda/AE0HV0QnHnmme5ntf5//vnnlq001ke9emussUaDvYZ6G/Ua7du3b7DXiLtHH300kfRrLNf1119fLekXlWJoTJYuRFWONmvWLLvuuuuaaI2zj3qyNQ32f//736ZeFQBZhMQfQFpq5fY++eSTJl0XLN80rkKJvuy9996Juuswas3+v//7P/fz888/b3/88UejrGe2U++XqBfEz1gHACT+ACK1cnt//vlntcGOGoD4+uuv25AhQ9wARJXBKGHTQHpPrbEqyVAZjp7XDFv777+/3X333aF30vZlRhpIud1227nfO+CAA1xddxgN7tX6HHLIIUmfHz16tJ188sm2ww47uIkBtFzViAd7Mfwypk+fniiD0v/1uB/cq/9rGcloTMBpp53mlq3X2GabbdzkBMmmJBbVsGt52l4aOKqSFvWy6M7fe+65pytxDG5zT/XuDz30kHuvW221VeL9/POf/6x1bfeUKVNswIAB7kJPy9NyNWua6rb9nc2DzjvvPLfuGkitgaUa9Oy3rb6rtV6PZ0KlKarfF5WYRaHB53//+9/tyiuvzGhQ5meffWbnnHOO+32tc/fu3d0+PXLkSLd9U/n000/d56ptpP1Z20m9D2G/N2/ePLv99ttdQq7PVxNVbL311m68mfatuXPnWn3S+9DyRZ+p36aZyOS49fuCd8wxx7j/63jRvqCfdRFSkx7Tc/pSyVay0iE9d9lll9XbvqoeJX3mOsY0HkHnmVReffVV9xr6fXqVkOtypsYfQNMJDpxcbbXVlnl+2LBh9uGHH7ryF10kqNVV5RfywQcfuATOz8aixzVwUzNnKOHWjC0axFlzBhAlSWr5VazqtjWo87vvvrOzzjrLJWiZUDKmpFSvJXotjRuaOnWqvfDCC+7eHkOHDrUdd9zRVl99dTe4WetWXl7uBpLq9fV4Opdffrk98MAD7ueVV17ZladonIBq0fWlRP7aa69NOj5ApS1KWoqLi902UiL47bffuguq//3vfy7J9zPXaJtoVpmXXnrJ/V/rqO2ucRhKUvSlbf7vf/878jZSAqZEXe+5pKTEbR9dcKiHR1+aFOGOO+5I+vlPmDDBJUSaXln7gNZHN0RUAq2LLSV/UbafaPIG0TZQSVUUBQUFyySG6ei9aP+qqKhws1UpqdNsb7r40pf2Fe0TwYte75133rG77rrLfQ7aTpoowm8n7Uu33nqr+xw97bdHH320mzJaFybaRhprpotLHQf60gWPtnEmk1WkK/XRhZAGPqvkR/vmoEGDIv9+psetntdxo/OAaLtou+rY8SVZGqC9zz77VHsd7dvB7VpzOm2/P2j2pvrYV/W7Wkc9p3XWhelGG23kZvdLRq+vxgHNUpTpMQVkJU3nCSB+pk6dWtm5c2f39c4776SM/b//+z8Xt8kmm1TOnDkz8fjhhx+eWMbtt9+eeHz27Nnu+08//VTZvXt39/yFF15YOXfu3ETM999/X3nggQe65w499NBqr/f++++7xzfYYIPKu+66q3Lx4sXu8QULFlRefvnlidfU18KFCxO/N3jwYPfYwQcfXG15t912m3t8s802q3z22WcrKyoqEssbMGCAe27zzTev/O233xK/s9NOO7nHR4wYUW1Zjz/+uHt8++23r/a41lOPb7zxxpUPPPBAYp3l+eefd8vX81r/IP86+jrvvPMq582b5x7XOmo5/rlXXnkl8Tuvv/66e2ybbbap/OqrrxKPL1q0qHLYsGHuuY022qhyxowZlVF8/PHHbr395/T7778nnpswYUJlnz593HP9+vWrtr3PPffcxPoddNBBld9++23iuQ8//LByiy22cM/997//jbQewe1xzjnnVNaV9utk+8mLL76YePzGG2+sLCsrSzz39ttvV2677bbuuZNOOqna8oL7+wEHHFA5bdq0xHNjxoxJvF8tM9nvaRv9/PPPicf1GT/55JOVG264oXten3eQf6233nor42P6u+++c48NHz486T5Uc/sE1fa4DVvnsWPHusd69eq1TPx2222X+J2TTz652nNTpkxxj2+99daJz68+9tXLLrvMHSvBc1WyfUXHWZcuXdxjQ4cOjfQZANmOUh8ASam7XC256k7X1ICiVsu2bdsuE6vWS5U9eP6GemoVVYuhylnU4rjSSislYtTqqRZVtQq+//77rlwo2IMg/fr1s2OPPTbR0q27dKv0RuUzUalVUL0HolpwzRCj1lC/vIsvvtjWXXdd11qt1v/aUNmDWnnlX//6lyvnCM4rr5Z+P8hSLffJyl/UO6AWWt/CrHXUcnwLqFpgva+++sp9V+lFsLxCLd8nnnii7bHHHq7cKmr5iMqJNEuOSoX0OQXv16DWULXsqmXVt0zXpBZh3T9B29HTuqmMRXwrcBRqnZaGnJ1HMzaJyoPUghtsnde+pfci6q3QvlmT9uPbbrvN7feeSkfUqyT333+/2598uYyf2lLbNjhltD5jzbfve7Dqcy79YMmPpsQWHcvq1UintsdtmJ49e7qpgfXZ+n1XtF10Px6V6eh40fLUA1OztV89cb6Eq677qo559RrqWJHgzT9r9kRoynCdP1QOphJBYHlA4g/ADaD0dbb+SzW9Srz99J0HHnhgaDe3kjyfTAep5ERqdu97uojo1auX+9nXpWtwp7r8Ra+fzMEHHxz5vSmZUCmGkjufiAYp4dCFgZIMJYK1oddQDbeSEz+osibVSeumXyo78glNkBLHZNvQl7voPXi+jEpJlxJQlZAE3XTTTa42O3hREEYJqq9xDhtIu/baaydKLZINFFX9c7KbNekmiTXXPZ0o95TceOONl9lf/ZdP6sOo7Eb14XLUUUeF7s/6Cnu/upBLljCqrEZJp96vv1DTPq79WSUoye4Kr/3BJ6/JatPrq+RHU9zqIkTJcjq1OW5T0bGnRF1U7uP5n1Wnr5tp6kJVjQ2eP078TcnqY1/VvqOLkFT0Gkr0dUGvxoJgowaQ66jxB5Coxw0mC2oZU526kin9IVWNfZhkSZ9qbv0AWbUQ6g7byfgY1bOL6tTVyiZKBpJRy16m4xOULCspS6auU3P6dVdte9jdjbVNlXSo5t8nnkFhNw/06xwcNKqWWLUSqxZdder6UpKtwdXbb7+9a2HV5xeFxjmoftkn8GH0nAZWJ1t3XdCkWne10EalfU4tw7pjchi1ENdcpgaGRpnRx39WSoRTjSHQ+9X0tcnerz7HZPz4DLVqa7C2PovgttC+rgHFulO8trtiVFvueweCrd31Sfu3WrnV66RWcPUIaVBrMrU9btPRPqsB7uPGjbN//OMf1RJ/7a/aHurx0EWStr0+S11Qa5v6gfT1sa9GuZuwavn9RViq/RDIRST+AFLewCuKZElmMAlLNltHTb5VOFieEjbQMVh6kI6fzSRdK19d+PeabCBokL8oSDZLT7DcJF1LuHoWVI7x4IMPuoGz2r5KwPSlwcV6HbVSakakZL0IydY93fqnWvf6vJmZLjSV+GtwcBiV0iQradGFUDr+/YZdoNXc95K931QDcP1zwdZ7fS4qs6m5floHzbyjcpdgCUxDOPzww91g8Pfee88uueSSxIw/9XXcpqMeLZXXqCdE28aX9rRq1cp95kr+tT8r8de+qwsEJfkq8/HbtD721SgXxGrpV6+OSv/uueced5G0+eabR3qfQLYj8QfQIIJ3zx01alTSMoewFl9Pf+iTlVSkmgI0bD2SJQH1xScm6ZIglQMF4+tCFwqaMlFfP/30k0uYVKLwxhtvuJKOG2+80bUy6/ko6+7XP6y23l+Q1desM2GU6CnpU422WnhVulGfkiWRmX5WvoU+Gb8P+ItTtRgr6dZ33VhOU06qx0A9NLrLvC7M1Brf0Im/L/lR+Y7WRbMgJZv2trbHbTpK8FU+pWRfFx/af7Ud9Xlr3dTw4C8M1Jvjxw74Mp/G3FfVM6Kpg1V2qHIjjd3QOKeovWhANqPGH0CDUOLjBwKnar1V977KHfwfayVH/g9ssN43yA+WjMIPOFXJT9gFg+b21sBltaLXhq9l12uEJZQq4/DvRyVBdaFt9fHHHydq+zU1oQaJas5zJSqaPlH89KXpykD8wMlUd2X2z9V13dPR+/AtthrIWd/8Z6WkLniviUzeb1h5ixJZX17iE2ZNLalEWxe0+lm140p2dUHje2NU/tUYfMmPqDXbTwdbH8dt1HIf0YWdr9VXS79/Xd3bQNtQ+7YSf/UK+N9pzH1V+6CoZ0QXD/q8NW4GWB6Q+ANoMOreF5WfJKtfVqudBunpD+3w4cPdY2qlVmLkE/JkNOd9VKoHV5mPxg2oBbMmrZeWpxtvBVtyfVIWZbCpXkN3kFVLpcoVklFttUpYtNxg7Xdt/Oc//3EDkTVXebKyGz9LTLqbUIm2jZ8lKayeWy3vmuFGwm5cVl+UAJ555pmJOdd1s6h0dEEVtdZcF4L+YtDvczVpFiLdoCvs/SppTtaDNGLECFeeojryrl27usf8DE66oE3We6XkWolu1M+rrtT74PcPzTBVX8dtlOPGJ/Gq7fdlTxqX4vmftV/rYkkTDARr8ht7X9W9J/yFkkp+/OcE5DISfwANRneh1R9rdd9rSrw5c+ZUGxyo51WDr3rd4Gw4uvOtEljNLqIbXvnBvkqq1PIWdhfcZNR6rNZ8UYu4TwpEtcZXXHGFKyvROgRn9fFjAvwgxlRUHqH34luplfwHEya1rGraUFGpR3Day9rQ7DGiO5CqBCGYZKk3xNfA+wuodDRtoVpS1RJ70UUXVeu1UAnK8ccf73pLNOWobw1tSNoXVLMvV199tdu2ujALblO9ZyViuiOryjJU3qT34Fv0U/GzU2n76fPy+5eoJVpTsoou0IKJqacWesUE92cNJvU3yNLgUD9mw6+PtmOwhV3rr7Is1bP7AavqhWhovuRH+3fYRW1tj9vgcaNB+mEXXdpH9dlpOtRgKZdP6v1sPsHW/qbaVw899FB3Ya99TyU/mZQZAtmIGn8ADUZd7ao1111mlRgp8dHsQEp0NK2iWsiVKGg6zWC9rsoklJyoZVvzcqtFXt38as1TwqHp/3SH1Kj++c9/uhIMtdSq1EIteWp91Tqo5Va9DEragjPrqA5bgxv1+io70AA/JXRhNFOJWnfVS6H66ZtvvtklNaq/1+BN2X333d0dR+tK66ILCLUwn3vuuS451ntSEqQZY5TQqcVZg3ujUO21LoA0yFvLVEu7ZrypWbqi+e3TDUKuL1oXDfrUHYG1/fWlsgsli6oFV2Lpy0yUzKp1V0lqlJp0DdzUdtLUn7ozslqtlZAqwfUXemoV10VnssHR+hx18amWce3P+j1fdqUWdSWLni5K1LKuMjBdLGj9Ve+ueLVq+x4atYA3VsmP9ku1ZIdN7Vnb49YfN6rh1zGgY0HbQtvAUzKvkjoty5f5eEqwdSz6gdHBu/U21b6qz1/bSRcR6lXSdtExB+QqEn8ADUqtzipzuffee+3NN990f5xV0qAESHOB6wZdyQZwahCipvNU4q1EQjXFSkjUG6ABf5kk/mohVJKnhHnkyJGuhV/LU9KiJE4tmDVb4fXHXS2wupGP1jlVPbhPEFQTrIsSJXpq0VQNtJI81dwr+UmWyNTWpZde6pIgtfjrvehLibGSJ90zQBcGmcy2o8Rm0003dZ+T3rNaZZXcbbnllu5mYFr/xh7cqHtH6IZr2n80X7xadJV4irarkkzdnEox/t4GUelGZ0o8lfRrwKmWrTIjPaZtof0veBO2IO0zGhiriwbtS35wqspfan7G6nHSPqfyFb0HXRyqd0LjMnThoHsJaDvr97QOuqBRWVBDU0u9EvqwmZBqe9zqgl3HgZ8K1X9eno5dP5amZuKvRF37r0qB1FMS1nvT2PuqLix00a+kX6+p84i/zwOQa/J0+96mXgkAAAAADYsafwAAACAGSPwBAACAGCDxBwAAAGKAxB8AAACIARJ/AAAAIAZI/AEAAIAYIPEHAAAAYoDEHwAAAIgBEn8AAAAgBkj8AQAAgBgg8QcAAABigMQfAAAAiAESfwAAAMCWf/8P1iIoQ9XxwD0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from bioneuralnet.utils import plot_performance\n",
+    "plot_performance(dpmon_predictions[1], raw_rf_acc, \"Raw Omics, vs DPMON Omics\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".test_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst b/docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst
new file mode 100644
index 0000000..01a96c0
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.clustering.correlated_louvain.rst
@@ -0,0 +1,12 @@
+bioneuralnet.clustering.correlated\_louvain
+===========================================
+
+.. automodule:: bioneuralnet.clustering.correlated_louvain
+
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+   
+      CorrelatedLouvain
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst b/docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst
new file mode 100644
index 0000000..83c0de8
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.clustering.correlated_pagerank.rst
@@ -0,0 +1,12 @@
+bioneuralnet.clustering.correlated\_pagerank
+============================================
+
+.. automodule:: bioneuralnet.clustering.correlated_pagerank
+
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+   
+      CorrelatedPageRank
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst b/docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst
new file mode 100644
index 0000000..1c27d14
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.clustering.hybrid_louvain.rst
@@ -0,0 +1,12 @@
+bioneuralnet.clustering.hybrid\_louvain
+=======================================
+
+.. automodule:: bioneuralnet.clustering.hybrid_louvain
+
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+   
+      HybridLouvain
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.clustering.rst b/docs/source/_autosummary/bioneuralnet.clustering.rst
new file mode 100644
index 0000000..7914ea2
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.clustering.rst
@@ -0,0 +1,15 @@
+bioneuralnet.clustering
+=======================
+
+.. automodule:: bioneuralnet.clustering
+
+   
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :recursive:
+
+   correlated_louvain
+   correlated_pagerank
+   hybrid_louvain
diff --git a/docs/source/_autosummary/bioneuralnet.datasets.dataset_loader.rst b/docs/source/_autosummary/bioneuralnet.datasets.dataset_loader.rst
new file mode 100644
index 0000000..e9ca75f
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.datasets.dataset_loader.rst
@@ -0,0 +1,12 @@
+bioneuralnet.datasets.dataset\_loader
+=====================================
+
+.. automodule:: bioneuralnet.datasets.dataset_loader
+
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+   
+      DatasetLoader
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.datasets.rst b/docs/source/_autosummary/bioneuralnet.datasets.rst
new file mode 100644
index 0000000..a322ac7
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.datasets.rst
@@ -0,0 +1,13 @@
+bioneuralnet.datasets
+=====================
+
+.. automodule:: bioneuralnet.datasets
+
+   
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :recursive:
+
+   dataset_loader
diff --git a/docs/source/_autosummary/bioneuralnet.downstream_task.DPMON.rst b/docs/source/_autosummary/bioneuralnet.downstream_task.DPMON.rst
index 916a883..5891adc 100644
--- a/docs/source/_autosummary/bioneuralnet.downstream_task.DPMON.rst
+++ b/docs/source/_autosummary/bioneuralnet.downstream_task.DPMON.rst
@@ -3,28 +3,25 @@ bioneuralnet.downstream\_task.dpmon
 
 .. automodule:: bioneuralnet.downstream_task.dpmon
 
-
+   
    .. rubric:: Functions
 
    .. autosummary::
-
+   
       build_omics_networks_tg
       run_hyperparameter_tuning
       run_standard_training
       setup_device
       slice_omics_datasets
       train_model
-
+   
    .. rubric:: Classes
 
    .. autosummary::
-
+   
       Autoencoder
       DPMON
       DimensionAveraging
       DownstreamTaskNN
-      GAT
-      GCN
-      GIN
       NeuralNetwork
-      SAGE
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.downstream_task.rst b/docs/source/_autosummary/bioneuralnet.downstream_task.rst
index fa6b9f4..8289be5 100644
--- a/docs/source/_autosummary/bioneuralnet.downstream_task.rst
+++ b/docs/source/_autosummary/bioneuralnet.downstream_task.rst
@@ -3,7 +3,7 @@
 
 .. automodule:: bioneuralnet.downstream_task
 
-
+   
 .. rubric:: Modules
 
 .. autosummary::
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.dynamic_visualization.rst b/docs/source/_autosummary/bioneuralnet.external_tools.dynamic_visualization.rst
deleted file mode 100644
index 4f9cd22..0000000
--- a/docs/source/_autosummary/bioneuralnet.external_tools.dynamic_visualization.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-bioneuralnet.external\_tools.dynamic\_visualization
-===================================================
-
-.. automodule:: bioneuralnet.external_tools.dynamic_visualization
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-
-      DynamicVisualizer
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.feature_selector.rst b/docs/source/_autosummary/bioneuralnet.external_tools.feature_selector.rst
deleted file mode 100644
index b64ec42..0000000
--- a/docs/source/_autosummary/bioneuralnet.external_tools.feature_selector.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-bioneuralnet.external\_tools.feature\_selector
-==============================================
-
-.. automodule:: bioneuralnet.external_tools.feature_selector
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-
-      FeatureSelector
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.hierarchical.rst b/docs/source/_autosummary/bioneuralnet.external_tools.hierarchical.rst
deleted file mode 100644
index 65bd5eb..0000000
--- a/docs/source/_autosummary/bioneuralnet.external_tools.hierarchical.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-bioneuralnet.external\_tools.hierarchical
-=========================================
-
-.. automodule:: bioneuralnet.external_tools.hierarchical
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-
-      HierarchicalClustering
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.node2vec.rst b/docs/source/_autosummary/bioneuralnet.external_tools.node2vec.rst
index c162fe4..f09ccc7 100644
--- a/docs/source/_autosummary/bioneuralnet.external_tools.node2vec.rst
+++ b/docs/source/_autosummary/bioneuralnet.external_tools.node2vec.rst
@@ -3,9 +3,10 @@ bioneuralnet.external\_tools.node2vec
 
 .. automodule:: bioneuralnet.external_tools.node2vec
 
-
+   
    .. rubric:: Classes
 
    .. autosummary::
-
+   
       node2vec
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.rst b/docs/source/_autosummary/bioneuralnet.external_tools.rst
index 3dbe4a1..3cdeb43 100644
--- a/docs/source/_autosummary/bioneuralnet.external_tools.rst
+++ b/docs/source/_autosummary/bioneuralnet.external_tools.rst
@@ -3,17 +3,13 @@
 
 .. automodule:: bioneuralnet.external_tools
 
-
+   
 .. rubric:: Modules
 
 .. autosummary::
    :toctree:
    :recursive:
 
-   dynamic_visualization
-   feature_selector
-   hierarchical
    node2vec
    smccnet
-   static_visualization
    wgcna
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.smccnet.rst b/docs/source/_autosummary/bioneuralnet.external_tools.smccnet.rst
index 3068f38..a6847ed 100644
--- a/docs/source/_autosummary/bioneuralnet.external_tools.smccnet.rst
+++ b/docs/source/_autosummary/bioneuralnet.external_tools.smccnet.rst
@@ -3,9 +3,10 @@ bioneuralnet.external\_tools.smccnet
 
 .. automodule:: bioneuralnet.external_tools.smccnet
 
-
+   
    .. rubric:: Classes
 
    .. autosummary::
-
+   
       SmCCNet
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.static_visualization.rst b/docs/source/_autosummary/bioneuralnet.external_tools.static_visualization.rst
deleted file mode 100644
index f450c54..0000000
--- a/docs/source/_autosummary/bioneuralnet.external_tools.static_visualization.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-bioneuralnet.external\_tools.static\_visualization
-==================================================
-
-.. automodule:: bioneuralnet.external_tools.static_visualization
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-
-      StaticVisualizer
diff --git a/docs/source/_autosummary/bioneuralnet.external_tools.wgcna.rst b/docs/source/_autosummary/bioneuralnet.external_tools.wgcna.rst
index 71de512..0a63d73 100644
--- a/docs/source/_autosummary/bioneuralnet.external_tools.wgcna.rst
+++ b/docs/source/_autosummary/bioneuralnet.external_tools.wgcna.rst
@@ -3,9 +3,10 @@ bioneuralnet.external\_tools.wgcna
 
 .. automodule:: bioneuralnet.external_tools.wgcna
 
-
+   
    .. rubric:: Classes
 
    .. autosummary::
-
+   
       WGCNA
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.metrics.check_variance.rst b/docs/source/_autosummary/bioneuralnet.metrics.check_variance.rst
new file mode 100644
index 0000000..7cd37b0
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.metrics.check_variance.rst
@@ -0,0 +1,19 @@
+bioneuralnet.metrics.check\_variance
+====================================
+
+.. automodule:: bioneuralnet.metrics.check_variance
+
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+   
+      network_remove_high_zero_fraction
+      network_remove_low_variance
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+   
+      CheckVariance
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.metrics.correlation.rst b/docs/source/_autosummary/bioneuralnet.metrics.correlation.rst
new file mode 100644
index 0000000..49a7891
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.metrics.correlation.rst
@@ -0,0 +1,14 @@
+bioneuralnet.metrics.correlation
+================================
+
+.. automodule:: bioneuralnet.metrics.correlation
+
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+   
+      compute_cluster_correlation_from_df
+      compute_correlation
+      convert_louvain_to_adjacency
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.metrics.rst b/docs/source/_autosummary/bioneuralnet.metrics.rst
new file mode 100644
index 0000000..50d86ac
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.metrics.rst
@@ -0,0 +1,14 @@
+bioneuralnet.metrics
+====================
+
+.. automodule:: bioneuralnet.metrics
+
+   
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :recursive:
+
+   check_variance
+   correlation
diff --git a/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_embedding.rst b/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_embedding.rst
index a08906c..3903ff1 100644
--- a/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_embedding.rst
+++ b/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_embedding.rst
@@ -3,9 +3,10 @@ bioneuralnet.network\_embedding.gnn\_embedding
 
 .. automodule:: bioneuralnet.network_embedding.gnn_embedding
 
-
+   
    .. rubric:: Classes
 
    .. autosummary::
-
+   
       GNNEmbedding
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst b/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst
index fc3a2b0..20f0d61 100644
--- a/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst
+++ b/docs/source/_autosummary/bioneuralnet.network_embedding.gnn_models.rst
@@ -3,12 +3,13 @@ bioneuralnet.network\_embedding.gnn\_models
 
 .. automodule:: bioneuralnet.network_embedding.gnn_models
 
-
+   
    .. rubric:: Classes
 
    .. autosummary::
-
+   
       GAT
       GCN
       GIN
       SAGE
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.network_embedding.rst b/docs/source/_autosummary/bioneuralnet.network_embedding.rst
index 128e335..9ce0f14 100644
--- a/docs/source/_autosummary/bioneuralnet.network_embedding.rst
+++ b/docs/source/_autosummary/bioneuralnet.network_embedding.rst
@@ -3,7 +3,7 @@
 
 .. automodule:: bioneuralnet.network_embedding
 
-
+   
 .. rubric:: Modules
 
 .. autosummary::
diff --git a/docs/source/_autosummary/bioneuralnet.subject_representation.rst b/docs/source/_autosummary/bioneuralnet.subject_representation.rst
index b83d7ea..36d261d 100644
--- a/docs/source/_autosummary/bioneuralnet.subject_representation.rst
+++ b/docs/source/_autosummary/bioneuralnet.subject_representation.rst
@@ -3,7 +3,7 @@
 
 .. automodule:: bioneuralnet.subject_representation
 
-
+   
 .. rubric:: Modules
 
 .. autosummary::
diff --git a/docs/source/_autosummary/bioneuralnet.subject_representation.subject_representation.rst b/docs/source/_autosummary/bioneuralnet.subject_representation.subject_representation.rst
index 9976643..c0bec98 100644
--- a/docs/source/_autosummary/bioneuralnet.subject_representation.subject_representation.rst
+++ b/docs/source/_autosummary/bioneuralnet.subject_representation.subject_representation.rst
@@ -3,9 +3,11 @@ bioneuralnet.subject\_representation.subject\_representation
 
 .. automodule:: bioneuralnet.subject_representation.subject_representation
 
-
+   
    .. rubric:: Classes
 
    .. autosummary::
-
+   
       GraphEmbedding
+      SimpleAE
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.utils.logger.rst b/docs/source/_autosummary/bioneuralnet.utils.logger.rst
index 38dab89..ddb3763 100644
--- a/docs/source/_autosummary/bioneuralnet.utils.logger.rst
+++ b/docs/source/_autosummary/bioneuralnet.utils.logger.rst
@@ -3,9 +3,10 @@ bioneuralnet.utils.logger
 
 .. automodule:: bioneuralnet.utils.logger
 
-
+   
    .. rubric:: Functions
 
    .. autosummary::
-
+   
       get_logger
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.utils.notebook_example.rst b/docs/source/_autosummary/bioneuralnet.utils.notebook_example.rst
new file mode 100644
index 0000000..2eb8c1b
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.utils.notebook_example.rst
@@ -0,0 +1,17 @@
+bioneuralnet.utils.notebook\_example
+====================================
+
+.. automodule:: bioneuralnet.utils.notebook_example
+
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+   
+      compare_clusters
+      evaluate_rf_classifier
+      evaluate_rf_regressor
+      plot_embeddings
+      plot_network
+      plot_performance
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.utils.path_utils.rst b/docs/source/_autosummary/bioneuralnet.utils.path_utils.rst
index 0f413fb..d633227 100644
--- a/docs/source/_autosummary/bioneuralnet.utils.path_utils.rst
+++ b/docs/source/_autosummary/bioneuralnet.utils.path_utils.rst
@@ -3,10 +3,11 @@ bioneuralnet.utils.path\_utils
 
 .. automodule:: bioneuralnet.utils.path_utils
 
-
+   
    .. rubric:: Functions
 
    .. autosummary::
-
+   
       get_r_script
       validate_paths
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.utils.rdata_to_csv.rst b/docs/source/_autosummary/bioneuralnet.utils.rdata_to_csv.rst
new file mode 100644
index 0000000..f1a625f
--- /dev/null
+++ b/docs/source/_autosummary/bioneuralnet.utils.rdata_to_csv.rst
@@ -0,0 +1,12 @@
+bioneuralnet.utils.rdata\_to\_csv
+=================================
+
+.. automodule:: bioneuralnet.utils.rdata_to_csv
+
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+   
+      rdata_to_csv_file
+   
\ No newline at end of file
diff --git a/docs/source/_autosummary/bioneuralnet.utils.rst b/docs/source/_autosummary/bioneuralnet.utils.rst
index 5958ec3..62e2df3 100644
--- a/docs/source/_autosummary/bioneuralnet.utils.rst
+++ b/docs/source/_autosummary/bioneuralnet.utils.rst
@@ -3,7 +3,7 @@
 
 .. automodule:: bioneuralnet.utils
 
-
+   
 .. rubric:: Modules
 
 .. autosummary::
@@ -11,4 +11,6 @@
    :recursive:
 
    logger
+   notebook_example
    path_utils
+   rdata_to_csv
diff --git a/docs/source/_static/ccrcc_search.png b/docs/source/_static/ccrcc_search.png
new file mode 100644
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diff --git a/docs/source/clustering.rst b/docs/source/clustering.rst
index e563594..838efad 100644
--- a/docs/source/clustering.rst
+++ b/docs/source/clustering.rst
@@ -1,6 +1,152 @@
-.. _clustering:
+Correlated Clustering Methods
+=============================
 
-Clustering for Multi-Omics
-==========================
+BioNeuralNet includes internal modules for performing **correlated clustering** on complex networks.
+These methods extend traditional community detection by integrating **phenotype correlation**, allowing users to extract **biologically relevant, phenotype-associated modules** from any network.
 
-**Coming soon**
+paper link: https://doi.org/10.3389/fdata.2022.894632 
+
+
+Overview
+--------
+
+Our framework supports three key **correlated clustering** approaches:
+
+- **Correlated PageRank**:
+
+  - A **modified PageRank algorithm** that prioritizes nodes based on their correlation with an external phenotype.
+  - The **personalization vector** is computed using phenotype correlation, ensuring that **biologically significant nodes receive more influence**.
+  - This method is ideal for **identifying high-impact nodes** within a given network.
+
+- **Correlated Louvain**:
+
+  - An adaptation of the **Louvain community detection algorithm**, modified to optimize for **both network modularity and phenotype correlation**.
+  - The objective function for community detection is given by:
+
+    .. math::
+
+       Q^* = k_L \cdot Q + (1 - k_L) \cdot \overline{\lvert \rho \rvert},
+
+    where:
+
+      - :math:`Q` is the standard **Newman-Girvan modularity**, defined as:
+
+        .. math::
+
+           Q = \frac{1}{2m} \sum_{i,j} \bigl(A_{ij} - \frac{k_i k_j}{2m} \bigr) \delta(c_i, c_j),
+
+        where :math:`A_{ij}` represents the adjacency matrix, :math:`k_i` and :math:`k_j` are node degrees, and :math:`\delta(c_i, c_j)` indicates whether nodes belong to the same community.
+      - :math:`\overline{\lvert \rho \rvert}` is the **mean absolute Pearson correlation** between the **first principal component (PC1) of the subgraph’s features** and the phenotype.
+      - :math:`k_L` is a user-defined weight (e.g., :math:`k_L = 0.2`), balancing **network modularity and phenotype correlation**.
+
+  - This method **detects communities** that are both **structurally cohesive and strongly associated with phenotype**.
+
+- **Hybrid Louvain**:
+
+  - A **refinement approach** that combines **Correlated Louvain** and **Correlated PageRank** in an iterative process.
+  - The key steps are:
+
+    1. **Initial Community Detection**:
+
+       - The **input network (adjacency matrix)** is clustered using **Correlated Louvain**.
+       - This identifies **initial phenotype-associated modules**.
+
+    2. **Iterative Refinement with Correlated PageRank**:
+
+       - In each iteration:
+
+         - The **most correlated module** is **expanded** based on Correlated PageRank.
+         - The refined network is **re-clustered using Correlated Louvain**.
+         - This process continues **until convergence**.
+
+    3. **Final Cluster Extraction**:
+
+       - The final **phenotype-optimized modules** are extracted and returned.
+       - The quality of the clustering is measured using **both modularity and phenotype correlation metrics**.
+
+  - This method ensures that **modules are refined over multiple iterations, yielding highly structured and phenotype-relevant clusters**.
+
+
+`View full-size image: Transforming Multi-Omics for Enhanced Disease Prediction <https://bioneuralnet.readthedocs.io/en/latest/_images/Overview.png>`_
+
+.. figure:: _static/hybrid_clustering.png
+   :align: center
+   :alt: Overview hybrid clustering workflow
+
+   **Hybrid Clustering**: Precedure and steps for the hybrid clustering method.
+
+
+Mathematical Formulation
+------------------------
+
+### **Correlated PageRank**
+- Correlated PageRank extends the traditional PageRank formulation by **biasing the random walk towards phenotype-associated nodes**.
+- The **ranking function** is defined as:
+
+  .. math::
+
+     \mathbf{r} = \alpha \cdot \mathbf{M} \mathbf{r} + (1 - \alpha) \mathbf{p},
+
+  where:
+
+  - :math:`\mathbf{M}` is the transition probability matrix, derived from the **normalized adjacency matrix**.
+  - :math:`\mathbf{p}` is the **personalization vector**, computed using **phenotype correlation**.
+  - :math:`\alpha` is the **teleportation factor** (default: :math:`\alpha = 0.85`).
+
+- Unlike standard PageRank, which assumes a **uniform teleportation distribution**, **Correlated PageRank prioritizes phenotype-relevant nodes**.
+
+Graphical Comparison
+--------------------
+
+Below is an illustration of **different clustering approaches** on a sample network:
+
+.. figure:: _static/clustercorrelation.png
+   :align: center
+   :alt: Comparison of Correlated Clustering Methods
+
+   **Figure 1:** Correlated Louvain extracts **phenotype-associated communities** by integrating modularity with phenotype correlation.
+
+Integration with BioNeuralNet
+------------------------------
+
+Our **correlated clustering methods** seamlessly integrate into **BioNeuralNet** and can be applied to **any network represented as an adjacency matrix**.
+
+Use cases include:
+
+   - **Multi-Omics Networks**: Extracting **biologically relevant subgraphs** from gene expression, proteomics, or metabolomics data.
+   - **Brain Connectivity Graphs**: Identifying **functional modules associated with neurological disorders**.
+   - **Social & Disease Networks**: Detecting **community structures in epidemiology and patient networks**.
+
+Our framework supports:
+
+   - **Graph Neural Network Embedding**: Training GNNs on **phenotype-optimized clusters**.
+   - **Predictive Biomarker Discovery**: Identifying key **features associated with disease outcomes**.
+   - **Customizable Modularity Optimization**: Allowing users to **adjust the trade-off between structure and phenotype correlation**.
+
+Notes for Users
+---------------
+
+1. **Input Requirements**:
+
+   - Any **graph-based dataset** can be used as input, provided as an **adjacency matrix**.
+   - Phenotype data should be supplied in **numerical format** (e.g., disease severity scores, expression levels).
+
+2. **Cluster Comparison**:
+
+   - **Correlated Louvain extracts phenotype-associated modules.**
+   - **Hybrid Louvain iteratively refines clusters using Correlated PageRank.**
+   - Users can compare results using **modularity scores and phenotype correlation metrics**.
+
+3. **Method Selection**:
+
+   - **Correlated PageRank** is ideal for **ranking high-impact nodes in a phenotype-aware manner**.
+   - **Correlated Louvain** is best for **detecting phenotype-associated communities**.
+   - **Hybrid Louvain** provides the most refined, **biologically meaningful clusters**.
+
+Conclusion
+----------
+
+The **correlated clustering methods** implemented in BioNeuralNet provide a **powerful, flexible framework** for extracting **highly structured, phenotype-associated modules** from any network.
+By integrating **phenotype correlation directly into the clustering process**, these methods enable **more biologically relevant and disease-informative network analysis**.
+
+Return to :doc:`../index`
diff --git a/docs/source/conf.py b/docs/source/conf.py
index a08fee2..0d2a556 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -7,7 +7,7 @@
 try:
     release = metadata.version("bioneuralnet")
 except metadata.PackageNotFoundError:
-    release = "0.1.0b1"
+    release = "0.2.0b2"
 
 project = "BioNeuralNet"
 version = release
@@ -18,6 +18,7 @@
     "sphinx.ext.viewcode",
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",
+    "nbsphinx",
 ]
 
 autosummary_generate = True
diff --git a/docs/source/examples/correlated_hybrid_louvain.py b/docs/source/examples/correlated_hybrid_louvain.py
new file mode 100644
index 0000000..1459600
--- /dev/null
+++ b/docs/source/examples/correlated_hybrid_louvain.py
@@ -0,0 +1,54 @@
+import networkx as nx
+import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.clustering import HybridLouvain
+from bioneuralnet.utils import compare_clusters
+
+# Load example synthetic dataset
+loader = DatasetLoader("example1")
+omics1, omics2, phenotype, clinical = loader.load_data()
+
+# Display dataset dimensions
+print("Dataset Shapes:")
+print(f"Omics1: {omics1.shape}")  # Expected: (358, 500)
+print(f"Omics2: {omics2.shape}")  # Expected: (358, 100)
+print(f"Phenotype: {phenotype.shape}")  # Expected: (358, 1)
+print(f"Clinical: {clinical.shape}")  # Expected: (358, 6)")
+
+# Generate global network using SmCCNet
+smccnet = SmCCNet(
+    phenotype_df=phenotype,
+    omics_dfs=[omics1, omics2],
+    data_types=["genes", "proteins"],
+    kfold=3,
+    subSampNum=500,
+)
+global_network, smccnet_clusters = smccnet.run()
+
+# Convert adjacency matrix to NetworkX graph
+merged_omics = pd.concat([omics1, omics2], axis=1)
+G_network = nx.from_pandas_adjacency(global_network)
+
+# Perform Hybrid Louvain Clustering
+hybrid_louvain_instance = HybridLouvain(
+    G=G_network,
+    B=merged_omics,
+    Y=phenotype,
+    k3=0.2,
+    k4=0.8,
+    max_iter=10,  # Number of refinement iterations
+    weight="weight",
+    tune=True
+)
+hybrid_louvain_results = hybrid_louvain_instance.run()
+
+# Extract final partitions
+final_clusters = hybrid_louvain_results["curr"]
+iterative_clusters = hybrid_louvain_results["clus"]
+
+print(f"Final Hybrid Louvain Clusters: {len(set(final_clusters.values()))}")
+print(f"Number of Iterative Refinements: {len(iterative_clusters)}")
+
+# Compare clusters against SmCCNet clusters
+compare_clusters(final_clusters, smccnet_clusters, phenotype)
diff --git a/docs/source/examples/correlated_louvain_example.py b/docs/source/examples/correlated_louvain_example.py
new file mode 100644
index 0000000..f8f0a55
--- /dev/null
+++ b/docs/source/examples/correlated_louvain_example.py
@@ -0,0 +1,47 @@
+import networkx as nx
+import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.clustering import CorrelatedLouvain
+from bioneuralnet.utils import compare_clusters
+
+# Load example synthetic dataset
+loader = DatasetLoader("example1")
+omics1, omics2, phenotype, clinical = loader.load_data()
+
+# Display dataset dimensions
+print("Dataset Shapes:")
+print(f"Omics1: {omics1.shape}")  # Expected: (358, 500)
+print(f"Omics2: {omics2.shape}")  # Expected: (358, 100)
+print(f"Phenotype: {phenotype.shape}")  # Expected: (358, 1)
+print(f"Clinical: {clinical.shape}")  # Expected: (358, 6)")
+
+# Generate global network using SmCCNet
+smccnet = SmCCNet(
+    phenotype_df=phenotype,
+    omics_dfs=[omics1, omics2],
+    data_types=["genes", "proteins"],
+    kfold=3,
+    subSampNum=500,
+)
+global_network, smccnet_clusters = smccnet.run()
+
+# Convert adjacency matrix to NetworkX graph
+merged_omics = pd.concat([omics1, omics2], axis=1)
+G_network = nx.from_pandas_adjacency(global_network)
+
+# Perform Correlated Louvain Clustering
+louvain_instance = CorrelatedLouvain(
+    G=G_network,
+    B=merged_omics,
+    Y=phenotype,
+    k3=0.2,
+    k4=0.8,
+    weight="weight",
+    tune=True
+)
+louvain_clusters = louvain_instance.run(as_dfs=True)
+
+# Compare clusters against SmCCNet clusters
+print(f"Number of Louvain Clusters: {len(louvain_clusters)}")
+compare_clusters(louvain_clusters, smccnet_clusters, phenotype)
diff --git a/docs/source/examples/dpmon_example.py b/docs/source/examples/dpmon_example.py
index 91f0f0e..db66e9c 100644
--- a/docs/source/examples/dpmon_example.py
+++ b/docs/source/examples/dpmon_example.py
@@ -1,30 +1,84 @@
+import numpy as np
 import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.network_embedding import GNNEmbedding
+from bioneuralnet.subject_representation import GraphEmbedding
 from bioneuralnet.downstream_task import DPMON
+from bioneuralnet.utils import evaluate_rf_classifier, plot_performance
 
+# Load example synthetic dataset
+loader = DatasetLoader("example1")
+omics1, omics2, phenotype, clinical = loader.load_data()
 
-def main():
-    adjacency_matrix = pd.read_csv("input/adjacency_matrix.csv", index_col=0)
-    protein_data = pd.read_csv("input/proteins.csv", index_col=0)
-    metabolite_data = pd.read_csv("input/metabolites.csv", index_col=0)
-    phenotype_df = pd.read_csv("input/phenotype_data.csv", index_col=0)
-    clinical_data_df = pd.read_csv("input/clinical_data.csv", index_col=0)
+# Display dataset dimensions
+print("Dataset Shapes:")
+print(f"Omics1: {omics1.shape}")  # Expected: (358, 500)
+print(f"Omics2: {omics2.shape}")  # Expected: (358, 100)
+print(f"Phenotype: {phenotype.shape}")  # Expected: (358, 1)
+print(f"Clinical: {clinical.shape}")  # Expected: (358, 6)")
 
-    dpmon = DPMON(
-        adjacency_matrix=adjacency_matrix,
-        omics_list=[protein_data, metabolite_data],
-        phenotype_file=phenotype_df,
-        features_file=clinical_data_df,
-        model="GCN",
-    )
+# Preprocess phenotype data: Convert continuous values into discrete bins
+min_val = phenotype["phenotype"].min()
+max_val = phenotype["phenotype"].max()
+bins = np.linspace(min_val, max_val, 5)  # Creates 4 categories
+phenotype["phenotype"] = pd.cut(phenotype["phenotype"], bins=bins, labels=[0, 1, 2, 3], include_lowest=True)
 
-    predictions_df = dpmon.run()
+print("Binned Phenotype Data:")
+print(phenotype.head())
+print(phenotype["phenotype"].value_counts(sort=False))
 
-    if not predictions_df.empty:
-        print("Disease prediction completed successfully. Sample predictions:")
-        print(predictions_df.head())
-    else:
-        print("Hyperparameter tuning completed (no predictions generated).")
+# Merge omics data
+merged_omics = pd.concat([omics1, omics2], axis=1)
 
+# Generate global network using SmCCNet
+smccnet = SmCCNet(
+    phenotype_df=phenotype,
+    omics_dfs=[omics1, omics2],
+    data_types=["genes", "proteins"],
+    kfold=3,
+    subSampNum=500,
+)
+global_network, smccnet_clusters = smccnet.run()
 
-if __name__ == "__main__":
-    main()
+# Generate node embeddings using GNNEmbedding
+embeddings = GNNEmbedding(
+    adjacency_matrix=global_network,
+    omics_data=merged_omics,
+    phenotype_data=phenotype,
+    clinical_data=clinical,
+    tune=True,
+)
+embeddings.fit()
+embeddings_output = embeddings.embed(as_df=True)
+
+# Perform Subject Representation using GraphEmbedding
+enhanced_omics = GraphEmbedding(
+    omics_data=merged_omics,
+    embeddings=embeddings_output,
+    phenotype_data=phenotype,
+    tune=True,
+)
+enhanced_omics_df = enhanced_omics.run()
+
+print(f"Enhanced Omics Shape: {enhanced_omics_df.shape}")
+
+# Run Disease Prediction using DPMON
+dpmon = DPMON(
+    adjacency_matrix=global_network,
+    omics_list=[omics1, omics2],
+    phenotype_data=phenotype,
+    clinical_data=clinical,
+    tune=True,
+)
+
+dpmon_predictions = dpmon.run()
+print(f"DPMON Predictions:\n{dpmon_predictions[0]}")
+
+# Evaluate Classifier Performance
+X_raw = merged_omics.values
+y_global = phenotype.values
+raw_rf_acc = evaluate_rf_classifier(X_raw, y_global)
+
+print("Global Results:")
+plot_performance(dpmon_predictions[1], raw_rf_acc, "Raw Omics vs. DPMON Omics")
diff --git a/docs/source/examples/dynamic_visualization_example.py b/docs/source/examples/dynamic_visualization_example.py
deleted file mode 100644
index 7cf95ae..0000000
--- a/docs/source/examples/dynamic_visualization_example.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import pandas as pd
-from bioneuralnet.external_tools import DynamicVisualizer
-
-
-def main():
-    adjacency_matrix = pd.read_csv("input/adjacency_matrix.csv", index_col=0)
-
-    dynamic_vis = DynamicVisualizer(
-        adjacency_matrix=adjacency_matrix,
-        layout="spring",
-        notebook=False,
-        bgcolor="#ffffff",
-        font_color="black",
-        output_dir="visualizations/dynamic",
-        output_filename="dynamic_network.html",
-        width="100%",
-        height="800px",
-    )
-
-    G = dynamic_vis.generate_graph()
-    dynamic_vis.visualize(G)
-    print("Dynamic visualization workflow completed successfully.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/docs/source/examples/example_1.py b/docs/source/examples/example_1.py
index afeebbd..b978bdc 100644
--- a/docs/source/examples/example_1.py
+++ b/docs/source/examples/example_1.py
@@ -3,74 +3,79 @@
 ======================================================================================================================
 
 Steps:
-1. Generate an adjacency matrix using SmCCNet based on multi-omics and phenotype data.
-2. Generate GNN node embeddings (GNNEmbedding) using the adjacency, omics, phenotype, and clinical data.
-3. Pass these precomputed embeddings into GraphEmbedding to integrate into omics data.
+1. Load synthetic multi-omics and phenotype data using DatasetLoader.
+2. Generate an adjacency matrix using SmCCNet based on multi-omics and phenotype data.
+3. Generate GNN node embeddings (GNNEmbedding) using the adjacency, omics, phenotype, and clinical data.
+4. Integrate the embeddings into omics data using GraphEmbedding to enhance feature representation.
 """
 
 import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
 from bioneuralnet.external_tools import SmCCNet
 from bioneuralnet.network_embedding import GNNEmbedding
 from bioneuralnet.subject_representation import GraphEmbedding
 
-
-def run_smccnet_workflow(
-    omics_data: pd.DataFrame, phenotype_data: pd.DataFrame, clinical_data: pd.DataFrame
-) -> pd.DataFrame:
+def run_smccnet_workflow() -> pd.DataFrame:
     """
-    Executes the SmCCNet-based workflow for generating enhanced omics data.
+    Executes the full SmCCNet-based workflow for generating enhanced omics data.
 
-    1) Instantiates SmCCNet, GNNEmbedding, and GraphEmbedding.
-    2) Generates an <ulti-Omics Network (adjacency matrix) via SmCCNet.
+    Steps:
+    1) Loads synthetic omics and phenotype data.
+    2) Generates a Multi-Omics Network (adjacency matrix) using SmCCNet.
     3) Runs GNNEmbedding to produce node embeddings.
-    4) Integrates the embeddings into omics data using GraphEmbedding.
-
-    Args:
-        omics_data (pd.DataFrame): Omics features (e.g., proteins, metabolites).
-        phenotype_data (pd.Series): Phenotype info (numeric/binary).
-        clinical_data (pd.DataFrame): Clinical info.
+    4) Integrates embeddings into omics data using GraphEmbedding.
 
     Returns:
         pd.DataFrame: Enhanced omics data integrated with GNN embeddings.
     """
     try:
-        smccnet_instance = SmCCNet(
-            phenotype_df=phenotype_data,
-            omics_dfs=omics_data,
-            data_types=["protein", "metabolite"],
-            kfold=5,
-            summarization="PCA",
-            seed=732,
+        # Load synthetic dataset
+        print("Loading dataset...")
+        loader = DatasetLoader("example1")
+        omics1, omics2, phenotype, clinical = loader.load_data()
+
+        # Display dataset dimensions
+        print("Dataset Shapes:")
+        print(f"Omics1: {omics1.shape}")
+        print(f"Omics2: {omics2.shape}")
+        print(f"Phenotype: {phenotype.shape}")
+        print(f"Clinical: {clinical.shape}")
+
+        # Merge omics data for processing
+        merged_omics = pd.concat([omics1, omics2], axis=1)
+
+        # Generate adjacency matrix using SmCCNet
+        print("Running SmCCNet...")
+        smccnet = SmCCNet(
+            phenotype_df=phenotype,
+            omics_dfs=[omics1, omics2],
+            data_types=["genes", "proteins"],
+            kfold=3,
+            subSampNum=500,
         )
-        adjacency_matrix = smccnet_instance.run()
+        global_network, smccnet_clusters = smccnet.run()
         print("Adjacency matrix generated using SmCCNet.")
 
+        # Generate node embeddings using GNNEmbedding
+        print("Running GNNEmbedding...")
         gnn_embedding = GNNEmbedding(
-            adjacency_matrix=adjacency_matrix,
-            omics_data=omics_data,
-            phenotype_data=phenotype_data,
-            clinical_data=clinical_data,
-            model_type="GCN",
-            hidden_dim=64,
-            layer_num=2,
-            dropout=True,
-            num_epochs=50,
-            lr=1e-3,
-            weight_decay=1e-4,
+            adjacency_matrix=global_network,
+            omics_data=merged_omics,
+            phenotype_data=phenotype,
+            clinical_data=clinical,
+            tune=True,
         )
         gnn_embedding.fit()
-        embeddings_tensor = gnn_embedding.embed()
-
-        node_names = adjacency_matrix.columns.tolist()
-        embeddings_df = pd.DataFrame(embeddings_tensor.numpy(), index=node_names)
-        print("GNN embeddings generated. Shape:", embeddings_df.shape)
+        embeddings_output = gnn_embedding.embed(as_df=True)
+        print(f"GNN embeddings generated. Shape: {embeddings_output.shape}")
 
+        # Integrate embeddings into omics data using GraphEmbedding
+        print("Running GraphEmbedding...")
         graph_embedding = GraphEmbedding(
-            adjacency_matrix=adjacency_matrix,
-            omics_data=omics_data,
-            phenotype_data=phenotype_data,
-            clinical_data=clinical_data,
-            embeddings=embeddings_df,
+            omics_data=merged_omics,
+            embeddings=embeddings_output,
+            phenotype_data=phenotype,
+            tune=True,
         )
         enhanced_omics_data = graph_embedding.run()
         print("Embeddings integrated into omics data.")
@@ -86,32 +91,13 @@ def run_smccnet_workflow(
     try:
         print("Starting SmCCNet + GNNEmbedding + GraphEmbedding Workflow...")
 
-        omics_data = pd.DataFrame(
-            {
-                "protein_feature1": [0.1, 0.2],
-                "protein_feature2": [0.3, 0.4],
-                "metabolite_feature1": [0.5, 0.6],
-                "metabolite_feature2": [0.7, 0.8],
-            },
-            index=["Sample1", "Sample2"],
-        )
-
-        phenotype_data = pd.Series([1, 0], index=["Sample1", "Sample2"])
-
-        clinical_data = pd.DataFrame(
-            {"clinical_feature1": [5, 3], "clinical_feature2": [7, 2]},
-            index=["Sample1", "Sample2"],
-        )
-        enhanced_omics = run_smccnet_workflow(
-            omics_data=omics_data,
-            phenotype_data=phenotype_data,
-            clinical_data=clinical_data,
-        )
+        enhanced_omics = run_smccnet_workflow()
 
         print("\nEnhanced Omics Data:")
-        print(enhanced_omics)
+        print(enhanced_omics.head())
+
+        print("\nSmCCNet workflow completed successfully.")
 
-        print("SmCCNet workflow completed successfully.\n")
     except Exception as e:
-        print(f"An error occurred during the execution: {e}")
+        print(f"An error occurred during execution: {e}")
         raise e
diff --git a/docs/source/examples/example_2.py b/docs/source/examples/example_2.py
index e73054c..041e57f 100644
--- a/docs/source/examples/example_2.py
+++ b/docs/source/examples/example_2.py
@@ -1,75 +1,77 @@
 """
-Example 2: Disease Prediction Using Graph Information (SmCCNet + Disease Prediction using Multi-Omics Networks (DPMON))
-========================================================================================================================
+Example 2: Disease Prediction Using Graph Information (SmCCNet + DPMON)
+=======================================================================
 
 This script demonstrates a workflow where we first generate a graph using Sparse Multiple Canonical Correlation Network
 (SmCCNet), and then use that network matrix to run Disease Prediction using Multi-Omics Networks (DPMON), a pipeline
-that leverages the power of Graph Neural Networks (GNNs) specifically designed to predict disease phenotypes.
+that leverages Graph Neural Networks (GNNs) for disease phenotype prediction.
 
 Steps:
-1. Generate an adjacency matrix using SmCCNet based on multi-omics and phenotype data.
-2. Utilize DPMON to predict disease phenotypes using the network information and omics data.
+1. Load synthetic multi-omics and phenotype data using DatasetLoader.
+2. Generate an adjacency matrix using SmCCNet based on multi-omics and phenotype data.
+3. Utilize DPMON to predict disease phenotypes using the network information and omics data.
 """
 
 import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
 from bioneuralnet.external_tools import SmCCNet
 from bioneuralnet.downstream_task import DPMON
 
-
-def run_smccnet_dpmon_workflow(
-    omics_proteins: pd.DataFrame,
-    omics_metabolites: pd.DataFrame,
-    phenotype_data: pd.DataFrame,
-    clinical_data: pd.DataFrame,
-) -> pd.DataFrame:
+def run_smccnet_dpmon_workflow() -> pd.DataFrame:
     """
     Executes the hybrid workflow combining SmCCNet for network generation and DPMON for disease prediction.
 
-    This function performs the following steps:
-        1. Generates an adjacency matrix using SmCCNet.
-        2. Initializes and runs DPMON for disease prediction based on the adjacency matrix.
-        3. Returns the disease prediction results.
-
-    Args:
-        omics_proteins (pd.DataFrame): DataFrame containing protein data.
-        omics_metabolites (pd.DataFrame): DataFrame containing metabolite data.
-        phenotype_data (pd.Series): Series containing phenotype information.
-        clinical_data (pd.DataFrame): DataFrame containing clinical data.
+    Steps:
+        1. Loads synthetic dataset (omics, phenotype, and clinical data).
+        2. Generates an adjacency matrix using SmCCNet.
+        3. Runs DPMON for disease prediction based on the adjacency matrix.
 
     Returns:
         pd.DataFrame: Disease prediction results from DPMON.
     """
     try:
-        smccnet_instance = SmCCNet(
-            phenotype_df=phenotype_data,
-            omics_dfs=[omics_proteins, omics_metabolites],
-            data_types=["protein", "metabolite"],
-            kfold=5,
-            summarization="PCA",
-            seed=732,
+        # Load synthetic dataset
+        print("Loading dataset...")
+        loader = DatasetLoader("example1")
+        omics1, omics2, phenotype, clinical = loader.load_data()
+
+        # Display dataset dimensions
+        print("Dataset Shapes:")
+        print(f"Omics1: {omics1.shape}")
+        print(f"Omics2: {omics2.shape}")
+        print(f"Phenotype: {phenotype.shape}")
+        print(f"Clinical: {clinical.shape}")
+
+        # Generate adjacency matrix using SmCCNet
+        print("Running SmCCNet...")
+        smccnet = SmCCNet(
+            phenotype_df=phenotype,
+            omics_dfs=[omics1, omics2],
+            data_types=["genes", "proteins"],
+            kfold=3,
+            subSampNum=500,
         )
-        adjacency_matrix = smccnet_instance.run()
+        global_network, smccnet_clusters = smccnet.run()
         print("Adjacency matrix generated using SmCCNet.")
 
-        dpmon_instance = DPMON(
-            adjacency_matrix=adjacency_matrix,
-            omics_list=[omics_proteins, omics_metabolites],
-            phenotype_data=phenotype_data,
-            clinical_data=clinical_data,
-            model="GCN",
-            tune=False,
-            gpu=False,
+        # Run Disease Prediction using DPMON
+        print("Running DPMON...")
+        dpmon = DPMON(
+            adjacency_matrix=global_network,
+            omics_list=[omics1, omics2],
+            phenotype_data=phenotype,
+            clinical_data=clinical,
+            tune=True,
         )
 
-        predictions_df = dpmon_instance.run()
-        if not predictions_df.empty:
+        dpmon_predictions = dpmon.run()
+
+        if not dpmon_predictions.empty:
             print("DPMON workflow completed successfully. Predictions generated.")
         else:
-            print(
-                "DPMON hyperparameter tuning completed. No predictions were generated."
-            )
+            print("DPMON hyperparameter tuning completed. No predictions were generated.")
 
-        return predictions_df
+        return dpmon_predictions
 
     except Exception as e:
         print(f"An error occurred during the SmCCNet + DPMON workflow: {e}")
@@ -78,33 +80,15 @@ def run_smccnet_dpmon_workflow(
 
 if __name__ == "__main__":
     try:
-        print("Starting SmCCNet + DPMON Hybrid Workflow...")
-
-        omics_proteins = pd.DataFrame(
-            {"protein_feature1": [0.1, 0.2], "protein_feature2": [0.3, 0.4]},
-            index=["Sample1", "Sample2"],
-        )
+        print("Starting SmCCNet + DPMON Workflow...")
 
-        omics_metabolites = pd.DataFrame(
-            {"metabolite_feature1": [0.5, 0.6], "metabolite_feature2": [0.7, 0.8]},
-            index=["Sample1", "Sample2"],
-        )
-
-        phenotype_data = pd.Series([1, 0], index=["Sample1", "Sample2"])
+        predictions = run_smccnet_dpmon_workflow()
 
-        clinical_data = pd.DataFrame(
-            {"clinical_feature1": [5, 3], "clinical_feature2": [7, 2]},
-            index=["Sample1", "Sample2"],
-        )
-
-        predictions = run_smccnet_dpmon_workflow(
-            omics_proteins, omics_metabolites, phenotype_data, clinical_data
-        )
+        print("\nDPMON Predictions:")
+        print(predictions.head())
 
-        print("DPMON Predictions:")
-        print(predictions)
+        print("\nHybrid Workflow completed successfully.")
 
-        print("Hybrid Workflow completed successfully.\n")
     except Exception as e:
-        print(f"An error occurred during the execution: {e}")
+        print(f"An error occurred during execution: {e}")
         raise e
diff --git a/docs/source/examples/feature_selector_example.py b/docs/source/examples/feature_selector_example.py
deleted file mode 100644
index 7aded66..0000000
--- a/docs/source/examples/feature_selector_example.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import pandas as pd
-from bioneuralnet.external_tools import FeatureSelector
-from bioneuralnet.network_embedding import GNNEmbedding
-
-
-def main():
-    phenotype_df = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "Phenotype": ["Control", "Treatment", "Control", "Treatment"],
-        }
-    )
-
-    omics_df1 = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "GeneA": [1.2, 2.3, 3.1, 4.0],
-            "GeneB": [2.1, 3.4, 1.2, 3.3],
-            "GeneC": [3.3, 1.5, 2.2, 4.1],
-        }
-    )
-
-    omics_df2 = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "GeneD": [4.2, 5.3, 6.1, 7.0],
-            "GeneE": [5.1, 6.4, 4.2, 6.3],
-            "GeneF": [6.3, 4.5, 5.2, 7.1],
-        }
-    )
-
-    clinical_data_df = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "Age": [30, 40, 50, 60],
-            "Sex": ["Male", "Female", "Female", "Male"],
-            "BMI": [25.0, 28.1, 30.2, 24.5],
-        }
-    )
-
-    adjacency_matrix = pd.DataFrame(
-        {
-            "GeneA": [1.0, 0.8, 0.3, 0.0],
-            "GeneB": [0.8, 1.0, 0.4, 0.0],
-            "GeneC": [0.3, 0.4, 1.0, 0.7],
-            "GeneD": [0.0, 0.0, 0.7, 1.0],
-        },
-        index=["GeneA", "GeneB", "GeneC", "GeneD"],
-    )
-
-    gnn_embed = GNNEmbedding(
-        omics_list=[omics_df1.set_index("SampleID"), omics_df2.set_index("SampleID")],
-        phenotype_df=phenotype_df.set_index("SampleID"),
-        clinical_data_df=clinical_data_df.set_index("SampleID"),
-        adjacency_matrix=adjacency_matrix,
-        model_type="GCN",
-    )
-    gnn_embeddings = gnn_embed.run()
-    print("\nGNN Embeddings:")
-    print(gnn_embeddings["graph"].head())
-
-    combined_omics_data = pd.merge(omics_df1, omics_df2, on="SampleID")
-    phenotype_series = phenotype_df.set_index("SampleID")["Phenotype"]
-
-    feature_selector = FeatureSelector(
-        enhanced_omics_data=combined_omics_data.set_index("SampleID"),
-        phenotype_data=phenotype_series,
-        num_features=20,
-        selection_method="lasso",
-    )
-    selected_features = feature_selector.run_feature_selection()
-    print("\nSelected Multi-Omics Features:")
-    print(selected_features.head())
-
-
-if __name__ == "__main__":
-    main()
diff --git a/docs/source/examples/gnn_embedding_example.py b/docs/source/examples/gnn_embedding_example.py
index da728c4..c4bfdef 100644
--- a/docs/source/examples/gnn_embedding_example.py
+++ b/docs/source/examples/gnn_embedding_example.py
@@ -1,69 +1,49 @@
 import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
 from bioneuralnet.network_embedding import GNNEmbedding
-
-
-def main():
-    phenotype_df = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "Phenotype": ["Control", "Treatment", "Control", "Treatment"],
-        }
-    )
-
-    omics_df1 = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "GeneA": [1.2, 2.3, 3.1, 4.0],
-            "GeneB": [2.1, 3.4, 1.2, 3.3],
-            "GeneC": [3.3, 1.5, 2.2, 4.1],
-        }
-    )
-
-    omics_df2 = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "GeneD": [4.2, 5.3, 6.1, 7.0],
-            "GeneE": [5.1, 6.4, 4.2, 6.3],
-            "GeneF": [6.3, 4.5, 5.2, 7.1],
-        }
-    )
-
-    clinical_data_df = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "Age": [30, 40, 50, 60],
-            "Sex": ["Male", "Female", "Female", "Male"],
-            "BMI": [25.0, 28.1, 30.2, 24.5],
-        }
-    )
-
-    adjacency_matrix = pd.DataFrame(
-        {
-            "GeneA": [1.0, 0.8, 0.3, 0.0],
-            "GeneB": [0.8, 1.0, 0.4, 0.0],
-            "GeneC": [0.3, 0.4, 1.0, 0.7],
-            "GeneD": [0.0, 0.0, 0.7, 1.0],
-        },
-        index=["GeneA", "GeneB", "GeneC", "GeneD"],
-    )
-
-    omics_data = pd.concat([omics_df1, omics_df2], axis=1)
-
-    gnn_embed = GNNEmbedding(
-        adjacency_matrix=adjacency_matrix,
-        omics_data=omics_data,
-        phenotype_df=phenotype_df.set_index("SampleID"),
-        clinical_data_df=clinical_data_df.set_index("SampleID"),
-        model_type="GCN",
-    )
-
-    print("Generating GNN embeddings...")
-    embeddings_dict = gnn_embed.run()
-    embeddings = embeddings_dict["graph"]
-
-    print("GNN Embeddings generated successfully.")
-    print(embeddings.head())
-
-
-if __name__ == "__main__":
-    main()
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.utils import plot_embeddings
+
+# Load example synthetic dataset
+loader = DatasetLoader("example1")
+omics1, omics2, phenotype, clinical = loader.load_data()
+
+# Display dataset dimensions
+print("Dataset Shapes:")
+print(f"Omics1: {omics1.shape}")  # Expected: (358, 500)
+print(f"Omics2: {omics2.shape}")  # Expected: (358, 100)
+print(f"Phenotype: {phenotype.shape}")  # Expected: (358, 1)
+print(f"Clinical: {clinical.shape}")  # Expected: (358, 6)")
+
+# Merge omics data
+merged_omics = pd.concat([omics1, omics2], axis=1)
+
+# Generate global network using SmCCNet
+smccnet = SmCCNet(
+    phenotype_df=phenotype,
+    omics_dfs=[omics1, omics2],
+    data_types=["genes", "proteins"],
+    kfold=3,
+    subSampNum=500,
+)
+global_network, smccnet_clusters = smccnet.run()
+
+# Initialize and run GNN Embedding
+embeddings = GNNEmbedding(
+    adjacency_matrix=global_network,
+    omics_data=merged_omics,
+    phenotype_data=phenotype,
+    clinical_data=clinical,
+    tune=True,
+)
+
+embeddings.fit()
+embeddings_output = embeddings.embed(as_df=True)
+
+# Display output shape
+print(f"GNN Embeddings Shape: {embeddings_output.shape}")
+
+
+global_node_labels = embeddings._prepare_node_labels()
+embeddings_array = embeddings_output.values  
+fig1 = plot_embeddings(embeddings_array, global_node_labels.to_frame(name="phenotype"), method="tsne")
diff --git a/docs/source/examples/hierarchical_example.py b/docs/source/examples/hierarchical_example.py
deleted file mode 100644
index dbf2bb8..0000000
--- a/docs/source/examples/hierarchical_example.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import pandas as pd
-from bioneuralnet.external_tools import HierarchicalClustering
-
-
-def main():
-    try:
-        print("Starting Hierarchical Clustering Workflow...")
-        adjacency_matrix = pd.DataFrame(
-            {
-                "GeneA": [1.0, 0.8, 0.3, 0.2],
-                "GeneB": [0.8, 1.0, 0.4, 0.3],
-                "GeneC": [0.3, 0.4, 1.0, 0.5],
-                "GeneD": [0.2, 0.3, 0.5, 1.0],
-            },
-            index=["GeneA", "GeneB", "GeneC", "GeneD"],
-        )
-
-        hierarchical_clustering = HierarchicalClustering(
-            adjacency_matrix=adjacency_matrix,
-            n_clusters=2,
-            linkage="ward",
-            affinity="euclidean",
-            scale_data=True,
-        )
-
-        clustering_results = hierarchical_clustering.run()
-
-        print("\nCluster Labels:")
-        print(clustering_results["cluster_labels"])
-
-        silhouette_score = clustering_results["silhouette_score"]
-        if silhouette_score is not None:
-            print(f"\nSilhouette Score: {silhouette_score:.4f}")
-        else:
-            print("\nSilhouette Score could not be computed.")
-
-        print("\nHierarchical Clustering Workflow completed successfully.")
-
-    except Exception as e:
-        print(f"An error occurred during execution: {e}")
-        raise e
-
-
-if __name__ == "__main__":
-    main()
diff --git a/docs/source/examples/pagerank_example.py b/docs/source/examples/pagerank_example.py
deleted file mode 100644
index 539cbb2..0000000
--- a/docs/source/examples/pagerank_example.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from bioneuralnet.clustering import PageRank
-import networkx as nx
-import pandas as pd
-
-
-def main():
-    graph = nx.read_edgelist("input/GFEV1ac110.edgelist", data=(("weight", float),))
-    omics = pd.read_csv("Input/X.csv").iloc[:, 1:]
-    phenotype = pd.read_csv("Input/Y.csv").iloc[:, 1]
-
-    omics.columns = [str(index) for index in range(omics.shape[1])]
-
-    pagerank_cluster = PageRank(
-        graph=graph,
-        omics_data=omics,
-        phenotype_data=phenotype,
-        alpha=0.9,
-        max_iter=100,
-        tol=1e-6,
-        k=0.9,
-    )
-
-    seed_nodes = [
-        "251",
-        "325",
-        "303",
-        "358",
-        "445",
-        "636",
-        "374",
-        "96",
-        "1159",
-        "324",
-        "2221",
-        "1884",
-        "1985",
-    ]
-    seed_nodes = [
-        "251",
-        "325",
-        "303",
-        "358",
-        "445",
-        "636",
-        "374",
-        "96",
-        "1159",
-        "324",
-        "2221",
-        "1884",
-        "1985",
-    ]
-    results = pagerank_cluster.run(seed_nodes=seed_nodes)
-    cluster_nodes = results["cluster_nodes"]
-    print(f"Identified cluster with {len(cluster_nodes)} nodes: {cluster_nodes}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/docs/source/examples/smccnet_example.py b/docs/source/examples/smccnet_example.py
index 27ecb48..0c589a1 100644
--- a/docs/source/examples/smccnet_example.py
+++ b/docs/source/examples/smccnet_example.py
@@ -1,57 +1,32 @@
 import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
 from bioneuralnet.external_tools import SmCCNet
 
-
-def main():
-    try:
-        print("Starting SmCCNet Workflow...")
-
-        phenotype_df = pd.DataFrame(
-            {
-                "SampleID": ["S1", "S2", "S3", "S4"],
-                "Phenotype": ["Control", "Treatment", "Control", "Treatment"],
-            }
-        )
-
-        omics_df1 = pd.DataFrame(
-            {
-                "SampleID": ["S1", "S2", "S3", "S4"],
-                "GeneA": [1.2, 2.3, 3.1, 4.0],
-                "GeneB": [2.1, 3.4, 1.2, 3.3],
-                "GeneC": [3.3, 1.5, 2.2, 4.1],
-            }
-        )
-
-        omics_df2 = pd.DataFrame(
-            {
-                "SampleID": ["S1", "S2", "S3", "S4"],
-                "GeneD": [4.2, 5.3, 6.1, 7.0],
-                "GeneE": [5.1, 6.4, 4.2, 6.3],
-                "GeneF": [6.3, 4.5, 5.2, 7.1],
-            }
-        )
-
-        omics_dfs = [omics_df1, omics_df2]
-        data_types = ["Transcriptomics", "Proteomics"]
-
-        smccnet = SmCCNet(
-            phenotype_df=phenotype_df,
-            omics_dfs=omics_dfs,
-            data_types=data_types,
-            kfold=5,
-            summarization="PCA",
-            seed=732,
-        )
-
-        adjacency_matrix = smccnet.run()
-
-        print("\nAdjacency Matrix:")
-        print(adjacency_matrix)
-
-    except Exception as e:
-        print(f"An error occurred during execution: {e}")
-        raise e
-
-
-if __name__ == "__main__":
-    main()
+# Load example synthetic dataset
+loader = DatasetLoader("example1")
+omics1, omics2, phenotype, clinical = loader.load_data()
+
+# Display dataset dimensions
+print("Dataset Shapes:")
+print(f"Omics1: {omics1.shape}")  # Expected: (358, 500)
+print(f"Omics2: {omics2.shape}")  # Expected: (358, 100)
+print(f"Phenotype: {phenotype.shape}")  # Expected: (358, 1)
+print(f"Clinical: {clinical.shape}")  # Expected: (358, 6)")
+
+# Merge omics and clinical data
+merged_omics = pd.concat([omics1, omics2, clinical, phenotype], axis=1)
+
+# Initialize and run SmCCNet
+smccnet = SmCCNet(
+    phenotype_df=phenotype,
+    omics_dfs=[omics1, omics2],
+    data_types=["genes", "proteins"],
+    kfold=3,
+    subSampNum=500,
+)
+
+global_network, smccnet_clusters = smccnet.run()
+
+# Display output sizes
+print(f"Global Network Shape: {global_network.shape}")
+print(f"Number of SmCCNet Clusters: {len(smccnet_clusters)}")
diff --git a/docs/source/examples/static_visualization_example.py b/docs/source/examples/static_visualization_example.py
deleted file mode 100644
index 9a860af..0000000
--- a/docs/source/examples/static_visualization_example.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import pandas as pd
-from bioneuralnet.external_tools import StaticVisualizer
-
-
-def main():
-    adjacency_matrix = pd.read_csv("input/adjacency_matrix.csv", index_col=0)
-
-    static_vis = StaticVisualizer(
-        adjacency_matrix=adjacency_matrix,
-        layout="spring",
-        node_size=300,
-        node_color="skyblue",
-        edge_color="gray",
-        linewidths=1.0,
-        font_size=10,
-        output_dir="visualizations/static",
-        output_filename="static_network.png",
-    )
-
-    G = static_vis.generate_graph()
-    static_vis.visualize(G)
-    print("Static visualization workflow completed successfully.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/docs/source/examples/subject_representation_example.py b/docs/source/examples/subject_representation_example.py
index df86c4b..8c53cf1 100644
--- a/docs/source/examples/subject_representation_example.py
+++ b/docs/source/examples/subject_representation_example.py
@@ -1,79 +1,54 @@
 import pandas as pd
+from bioneuralnet.datasets import DatasetLoader
+from bioneuralnet.external_tools import SmCCNet
+from bioneuralnet.network_embedding import GNNEmbedding
 from bioneuralnet.subject_representation import GraphEmbedding
 
-
-def main():
-    phenotype_df = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "Phenotype": ["Control", "Treatment", "Control", "Treatment"],
-        },
-        index_col=0,
-    )
-
-    omics_df1 = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "GeneA": [1.2, 2.3, 3.1, 4.0],
-            "GeneB": [2.1, 3.4, 1.2, 3.3],
-            "GeneC": [3.3, 1.5, 2.2, 4.1],
-        },
-        index_col=0,
-    )
-
-    omics_df2 = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "GeneD": [4.2, 5.3, 6.1, 7.0],
-            "GeneE": [5.1, 6.4, 4.2, 6.3],
-            "GeneF": [6.3, 4.5, 5.2, 7.1],
-        },
-        index_col=0,
-    )
-
-    # Example Clinical DataFrame
-    clinical_data_df = pd.DataFrame(
-        {
-            "SampleID": ["S1", "S2", "S3", "S4"],
-            "Age": [30, 40, 50, 60],
-            "Sex": ["Male", "Female", "Female", "Male"],
-            "BMI": [25.0, 28.1, 30.2, 24.5],
-        },
-        index_col=0,
-    )
-
-    adjacency_matrix = pd.DataFrame(
-        {
-            "GeneA": [1.0, 0.8, 0.3, 0.0],
-            "GeneB": [0.8, 1.0, 0.4, 0.0],
-            "GeneC": [0.3, 0.4, 1.0, 0.7],
-            "GeneD": [0.0, 0.0, 0.7, 1.0],
-        },
-        index=["GeneA", "GeneB", "GeneC", "GeneD"],
-        index_col=0,
-    )
-
-    # You can also load the data from files directly
-
-    # omics_files = [pd.read_csv('input/proteins.csv', index_col=0),
-    #                pd.read_csv('input/metabolites.csv', index_col=0)]
-    # phenotype_df = pd.read_csv('input/phenotype_data.csv', index_col=0)
-    # clinical_data_df = pd.read_csv('input/clinical_data.csv', index_col=0)
-    # adjacency_matrix = pd.read_csv('input/adjacency_matrix.csv', index_col=0)
-
-    graph_embed = GraphEmbedding(
-        adjacency_matrix=adjacency_matrix,
-        omics_list=[omics_df1, omics_df2],
-        phenotype_df=phenotype_df,
-        clinical_data_df=clinical_data_df,
-        embeddings="GNNs",
-    )
-
-    enhanced_omics_data = graph_embed.run()
-
-    enhanced_omics_data.to_csv("output/enhanced_omics_data.csv")
-    print("Graph embedding workflow completed successfully.")
-
-
-if __name__ == "__main__":
-    main()
+# Load example synthetic dataset
+loader = DatasetLoader("example1")
+omics1, omics2, phenotype, clinical = loader.load_data()
+
+# Display dataset dimensions
+print("Dataset Shapes:")
+print(f"Omics1: {omics1.shape}")  # Expected: (358, 500)
+print(f"Omics2: {omics2.shape}")  # Expected: (358, 100)
+print(f"Phenotype: {phenotype.shape}")  # Expected: (358, 1)
+print(f"Clinical: {clinical.shape}")  # Expected: (358, 6)")
+
+# Merge omics data
+merged_omics = pd.concat([omics1, omics2], axis=1)
+
+# Generate global network using SmCCNet
+smccnet = SmCCNet(
+    phenotype_df=phenotype,
+    omics_dfs=[omics1, omics2],
+    data_types=["genes", "proteins"],
+    kfold=3,
+    subSampNum=500,
+)
+global_network, smccnet_clusters = smccnet.run()
+
+# Generate node embeddings using GNNEmbedding
+embeddings = GNNEmbedding(
+    adjacency_matrix=global_network,
+    omics_data=merged_omics,
+    phenotype_data=phenotype,
+    clinical_data=clinical,
+    tune=True,
+)
+embeddings.fit()
+embeddings_output = embeddings.embed(as_df=True)
+
+# Perform Subject Representation using GraphEmbedding
+enhanced_omics = GraphEmbedding(
+    omics_data=merged_omics,
+    embeddings=embeddings_output,
+    phenotype_data=phenotype,
+    tune=True,
+)
+
+enhanced_omics_df = enhanced_omics.run()
+
+# Display output shape and preview
+print(f"Enhanced Omics Shape: {enhanced_omics_df.shape}")
+print(enhanced_omics_df.head())
diff --git a/docs/source/external_tools/Analysis_and_Visualization.rst b/docs/source/external_tools/Analysis_and_Visualization.rst
deleted file mode 100644
index 59b024e..0000000
--- a/docs/source/external_tools/Analysis_and_Visualization.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-Analysis and Visualization
-==========================
-
-BioNeuralNet includes several classes in `external_tools` that can complement the main pipeline:
-
-**FeatureSelector**:
-  - Provides quick methods (LassoCV, RandomForestClassifier) to select top features from a dataset.
-
-.. literalinclude:: ../examples/feature_selector_example.py
-   :language: python
-   :caption: Using FeatureSelector for simple feature ranking.
-
-**StaticVisualizer**:
-  - Uses NetworkX + Matplotlib to produce static images of your network.
-
-.. literalinclude:: ../examples/static_visualization_example.py
-   :language: python
-   :caption: Creating a static network image.
-
-**DynamicVisualizer**:
-  - Employs PyVis or other interactive libs to build HTML-based network visualizations.
-
-.. literalinclude:: ../examples/dynamic_visualization_example.py
-   :language: python
-   :caption: Generating an interactive HTML network visualization.
-
-**HierarchicalClustering**:
-  - Performs agglomerative clustering, silhouette scoring, etc., on adjacency or embedded data.
-
-.. note::
-   For deeper clustering examples (like PageRank-based sweeps), check :doc:`../tools/index`
-   or the `examples/` folder.
diff --git a/docs/source/external_tools/index.rst b/docs/source/external_tools/index.rst
index 2e77e6f..227ac16 100644
--- a/docs/source/external_tools/index.rst
+++ b/docs/source/external_tools/index.rst
@@ -6,12 +6,9 @@ BioNeuralNet provides convenience wrappers for external tools, including:
 - **SmCCNet** and **WGCNA** (R-based methods for network construction)
 - **Node2Vec** (random-walk-based embedding)
 - **FeatureSelector** (basic Python-based approaches for feature selection)
-- **Visualization** (StaticVisualizer, DynamicVisualizer)
-- **HierarchicalClustering** for network clustering
 
 .. toctree::
    :maxdepth: 2
    :caption: External Tools
 
    Graph_Construction
-   Analysis_and_Visualization
diff --git a/docs/source/faq.rst b/docs/source/faq.rst
index 6f00a8d..1df3455 100644
--- a/docs/source/faq.rst
+++ b/docs/source/faq.rst
@@ -3,80 +3,72 @@ Frequently Asked Questions (FAQ)
 
 **Q1: What is BioNeuralNet?**
 
-A1: BioNeuralNet is a Python-based framework that integrates **multi-omics** data with **Graph Neural Networks (GNNs)**
-for clustering, embedding, and disease prediction. It converts high-dimensional omics networks into lower-dimensional
-representations, enabling downstream tasks like subject representation, subnetwork discovery, and phenotype prediction.
+A1: BioNeuralNet is a Python framework for integrating **multi-omics data** with **Graph Neural Networks (GNNs)**.  
+It provides tools for **graph construction, clustering, network embedding, subject representation, and disease prediction**.
 
-**Q2: What are the key components of BioNeuralNet?**
+**Q2: What are the key features of BioNeuralNet?**
 
-A2: BioNeuralNet covers five main steps:
+A2: BioNeuralNet includes the following components:
 
-1. **Graph Construction**: (External) Build adjacency matrices using WGCNA, SmCCNet, or your own methods.
-2. **Graph Clustering**: Identify modules via hierarchical clustering, PageRank, or Louvain.
-3. **Network Embedding**: Generate embeddings with GNNs or Node2Vec.
-4. **Subject Representation**: Integrate node embeddings into omics data for enhanced feature sets.
-5. **Downstream Tasks**: E.g., disease prediction using **DPMON**, an end-to-end pipeline that trains GNN + NN.
+- **Graph Construction**: Build multi-omics networks using **SmCCNet** or custom adjacency matrices.
+- **Graph Clustering**: Identify meaningful communities with **Louvain, Hybrid Louvain, or PageRank-based methods**.
+- **GNN Embedding**: Learn **node embeddings** from biological graphs with `GNNEmbedding`.
+- **Subject Representation**: Integrate embeddings into omics data via `GraphEmbedding` for enhanced feature learning.
+- **Disease Prediction**: Use **DPMON**, an end-to-end pipeline that trains a GNN-based classifier.
 
-**Q3: How do I install BioNeuralNet and dependencies?**
+**Q3: How do I install BioNeuralNet?**
 
-A3: Simply do:
+A3: Install BioNeuralNet with:
 
 .. code-block:: bash
 
-   pip install bioneuralnet==0.1.0b1
+   pip install bioneuralnet
 
-Then install PyTorch separately from [PyTorch.org](https://pytorch.org/get-started/locally/).
-Optionally install R-based packages (WGCNA, SmCCNet) if you want to construct adjacency matrices with them.
-See :doc:`installation` for more details.
+For GPU support, install PyTorch with CUDA from [PyTorch.org](https://pytorch.org/get-started/locally/).  
+See :doc:`installation` for full setup details.
 
 **Q4: Does BioNeuralNet support GPU acceleration?**
 
-A4: Yes. Install a GPU-compatible version of PyTorch (with CUDA) from the PyTorch site.
-BioNeuralNet will automatically detect and use CUDA if `torch.cuda.is_available()` returns True.
+A4: Yes. If you have a **CUDA-compatible GPU**, BioNeuralNet will automatically use it if `torch.cuda.is_available()` is `True`.
 
-**Q5: Can I contribute custom components (e.g., new GNN architectures)?**
+**Q5: Can I use my own adjacency matrix instead of SmCCNet?**
 
-A5: Definitely! We welcome contributions. Fork the repository, add your module or architecture,
-and submit a pull request. Check the `README <https://github.com/UCD-BDLab/BioNeuralNet/blob/main/README.md>`_
-and see instructions on code style, testing, and docstrings.
+A5: Yes! If you have a **precomputed adjacency matrix**, you can pass it directly to `GNNEmbedding` or `DPMON`.  
+SmCCNet is an optional tool for generating adjacency matrices.
 
-**Q6: How does DPMON differ from a standard GNN approach?**
+**Q6: How is DPMON different from other GNN models?**
 
-A6: **DPMON** is an end-to-end pipeline that trains both a GNN (for node embeddings) and a neural
-network classifier jointly, focusing on **local + global** structure for disease phenotypes. It does
-*not* rely on correlation-based node labeling; rather, it fuses multi-omics data with the adjacency
-matrix to learn disease-predictive embeddings in a single integrated model.
+A6: **DPMON** is designed for **multi-omics disease prediction**. Unlike standard GNNs, it:  
+   - Jointly learns **node embeddings and a classifier**.  
+   - Leverages both **local and global graph structures**.  
+   - Integrates **phenotype and clinical data** alongside omics features.
 
-**Q7: Can BioNeuralNet work with unsupervised GNN embeddings?**
+**Q7: Can I run GNNEmbedding without labeled data (unsupervised learning)?**
 
-A7: Yes. If you do not provide a numeric label for each node, the GNN can run unsupervised (via
-basic adjacency-based features). For truly self-supervised tasks (e.g., contrastive learning),
-you might adapt code or rely on external libraries. But you can definitely skip explicit labels
-and still get a structural embedding.
+A7: Yes! If you don’t provide labels, **GNNEmbedding** will still generate embeddings based on graph structure.  
+For **self-supervised learning** (e.g., contrastive learning), you may need additional adaptation.
 
-**Q8: Where are the advanced clustering or visualization methods found?**
+**Q8: What clustering methods does BioNeuralNet support?**
 
-A8: We provide classes like `PageRank`, `HierarchicalClustering`, `StaticVisualizer`,
-and `DynamicVisualizer` under `bioneuralnet.external_tools` or within the pipeline’s
-tool suite. These methods are separate from the core embedding logic but integrate seamlessly
-once you have adjacency matrices or embedded data.
+A8: BioNeuralNet provides:  
+   - **Correlated Louvain**: Clusters nodes based on omics similarity and phenotype correlation.  
+   - **Hybrid Louvain**: Iteratively refines clusters using **PageRank expansion**.  
+   - **Correlated PageRank**: Detects communities based on personalized PageRank scores.
 
-**Q9: I have existing adjacency matrices. Do I need WGCNA or SmCCNet?**
+**Q9: Can I contribute new features or models?**
 
-A9: Not necessarily. If your adjacency matrix is already built, you can pass it directly to
-GNNEmbedding or DPMON. The external tool wrappers (WGCNA, SmCCNet) are optional conveniences
-for generating adjacency matrices in R.
+A9: Yes! We welcome contributions. Fork the repository, add your module, and submit a pull request.  
+Check our `contribution guide <https://github.com/UCD-BDLab/BioNeuralNet/blob/main/README.md>`_.
 
-**Q10: What license is BioNeuralNet released under?**
+**Q10: What license is BioNeuralNet under?**
 
-A10: BioNeuralNet is under the `MIT License <https://github.com/UCD-BDLab/BioNeuralNet/blob/main/LICENSE>`_.
+A10: BioNeuralNet is released under the `MIT License <https://github.com/UCD-BDLab/BioNeuralNet/blob/main/LICENSE>`_.
 
 **Q11: How do I report issues or request features?**
 
-A11: Please open an Issue on our GitHub repository `UCD-BDLab/BioNeuralNet <https://github.com/UCD-BDLab/BioNeuralNet/issues>`_.
-We track bugs, enhancements, and feature requests there.
+A11: Open an issue on our GitHub repository:  
+`UCD-BDLab/BioNeuralNet <https://github.com/UCD-BDLab/BioNeuralNet/issues>`_.
 
-**Q12: Where can I find more examples or tutorials?**
+**Q12: Where can I find tutorials or example scripts?**
 
-A12: Check out :doc:`tutorials/index` for step-by-step workflows demonstrating
-graph construction, embedding generation, subject representation, and disease prediction.
+A12: See :doc:`tutorials/index` for **step-by-step guides** on graph construction, embeddings, subject representation, and disease prediction.
diff --git a/docs/source/future.rst b/docs/source/future.rst
new file mode 100644
index 0000000..b617ef0
--- /dev/null
+++ b/docs/source/future.rst
@@ -0,0 +1,132 @@
+Coming Soon: Multi-Modal Support 
+================================
+
+Overview
+--------
+At its current state, BioNeuralNet can be used to reduce the dimensionality of high-dimensional omics data, but it also offers flexibility to explore additional modalities.
+
+In this example, we will use the **CPTAC data repository**:  
+[https://github.com/PayneLab/cptac](https://github.com/PayneLab/cptac)  
+to quickly show an example.
+
+The **CPTAC Data** module is a Python interface developed by the Payne Lab that provides fast access to the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) data. This package delivers comprehensive multi‐omics and clinical data (e.g. proteomics, genomics, transcriptomics, and clinical attributes) as native Python *pandas* DataFrames. The tool allows you to rapidly download, inspect, and export data from various cancer types—including clear cell renal cell carcinoma (CCRCC), breast, colon, ovarian, and more—without manual parsing or reformatting.
+
+.. note::
+
+   **NOTE:** Although the CPTAC data module is not part of the core BioNeuralNet package, it offers an extremely convenient and fast way to pull omics data in Python. This makes it a great starting point for users who want to integrate omics data into their analysis pipelines or pair these data with imaging and clinical datasets for multimodal research.
+
+   We highly encourage you to review their documentation at [CPTAC data](https://pypi.org/project/cptac/). Below, we offer a simple example of how flexible BioNeuralNet is when working with external packages.
+
+CPTAC Example
+-------------
+To install the package from PyPI, run:
+
+.. code-block:: bash
+
+   pip install cptac
+
+Usage Example: Clear Cell Renal Cell Carcinoma
+----------------------------------------------
+The following example shows how to list available cancers,  
+load the CCRCC dataset, and extract multiple data types.
+
+.. code-block:: python
+
+   import cptac
+
+   ccrcc = cptac.Ccrcc()
+
+   proteomics = ccrcc.get_proteomics("bcm")
+   genomics = ccrcc.get_dataframe("CNV", "bcm")
+   clinical = ccrcc.get_clinical("mssm")
+
+   proteomics.to_csv("ccrcc_output/ccrcc_proteomics_bcm.csv")
+   genomics.to_csv("ccrcc_output/ccrcc_genomics_cnv_bcm.csv")
+   clinical.to_csv("ccrcc_output/ccrcc_clinical_mssm.csv")
+
+Output Examples
+---------------
+Exporting the data to CSV to take a closer look.
+
+- **CCRCC Data format**
+
+  .. figure:: _static/cptac_clinical.png
+     :align: center
+     :alt: CPTAC Clinical Data
+
+  .. figure:: _static/cptac_genomics.png
+     :align: center
+     :alt: CPTAC Genomics Data
+
+  .. figure:: _static/cptac_proteomics.png
+     :align: center
+     :alt: CPTAC Proteomics Data
+
+Integration with BioNeuralNet
+-----------------------------
+Since the `get_data()` functions from `ccrcc` return a pandas DataFrame, integrating CPTAC data into BioNeuralNet is seamless because most components work with DataFrames.
+
+.. code-block:: python
+
+   import cptac
+   from BioNeuralNet import external_tools.SmCCNet
+
+   # Load the CCRCC dataset
+   ccrcc = cptac.Ccrcc()
+
+   # Retrieve omics and clinical data
+   genomics = ccrcc.get_dataframe("CNV", "bcm")
+   proteomics = ccrcc.get_proteomics("bcm")
+   clinical = ccrcc.get_clinical("mssm")
+
+   smccnet = SmCCNet(
+       phenotype_df=clinical["tumor_stage_pathological"],
+       omics_dfs=[genomics, proteomics],
+       data_types=["Genes", "Proteins"],
+   )
+
+- **Ease of Integration:** As demonstrated in the BioNeuralNet documentation, the CPTAC Data module can be integrated as a data retrieval engine, feeding high-quality, reproducible data into advanced machine learning or network analysis pipelines.
+
+Integration with Other Data Sources
+-----------------------------------
+Beyond omics data, the CPTAC Data module serves as an excellent entry point for multimodal research. For example, researchers can combine omics data obtained via this module with imaging data available from the **Cancer Imaging Archive**. This enables studies that integrate molecular and imaging information—vital for the development of comprehensive cancer diagnostics and treatment strategies.
+
+For example, we can look at the **NCI Cancer Imaging Archive** to get additional modalities.  
+Since we are analyzing **Clear Cell Renal Cell Carcinoma (CCRCC)**, we can search for `ccrcc` in the collection:  
+[NCI Cancer Imaging Archive – CCRCC Collection](https://www.cancerimagingarchive.net/collection/cptac-ccrcc/)  
+We can then retrieve the respective images for the patients.
+
+.. figure:: _static/ccrcc_search.png
+   :align: center
+   :alt: CCRCC Search in Cancer Imaging Archive
+
+.. figure:: _static/images_download.png
+   :align: center
+   :alt: Image Download Process
+
+If you are working with another cancer type, there are many other opportunities.
+
+.. figure:: _static/cptac_search.png
+   :align: center
+   :alt: CPTAC Data Search
+
+Conclusion
+----------
+BioNeuralNet aims to assist researchers in their work and guide future development.
+
+References
+----------
+- **PayneLab/cptac GitHub Repository:**  
+  [https://github.com/PayneLab/cptac](https://github.com/PayneLab/cptac)
+
+- **Cancer Imaging Archive – Imaging-Omics:**  
+  [https://www.cancerimagingarchive.net/imaging-omics/](https://www.cancerimagingarchive.net/imaging-omics/)
+
+- **NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) – CCRCC Collection (Version 13):**  
+  National Cancer Institute, The Cancer Imaging Archive.  
+  [https://doi.org/10.7937/k9/tcia.2018.oblamn27](https://doi.org/10.7937/k9/tcia.2018.oblamn27)
+
+- **Edwards NJ, Oberti M, Thangudu RR, et al. (2015).**  
+  *The CPTAC Data Portal: A Resource for Cancer Proteomics Research.*  
+  J Proteome Res. 14(6):2707-13.  
+  [DOI: 10.1021/pr501254j](https://doi.org/10.1021/pr501254j)
diff --git a/docs/source/gnns.rst b/docs/source/gnns.rst
index c73e3e3..5b91155 100644
--- a/docs/source/gnns.rst
+++ b/docs/source/gnns.rst
@@ -1,9 +1,7 @@
-.. _gnns:
+GNN Embeddings for Multi-Omics
+==============================
 
-GNNs for Multi-Omics
-====================
-
-This section provides an overview of **Graph Neural Networks (GNNs)** in BioNeuralNet for multi-omics data. We support four GNN architectures (GCN, GAT, GraphSAGE, GIN), each of which can be **task-driven** (using numeric values or other labels per node) or **unsupervised** (using intrinsic objectives) to produce node embeddings.
+This section provides an overview of **Graph Neural Networks (GNNs)** in BioNeuralNet for multi-omics data—focusing on task-driven, supervised or semi-supervised approaches. In our framework, each node (representing an omics feature such as a gene or protein) is assigned a numeric label computed as the Pearson correlation between its expression values and the phenotype (or clinical variable) of interest. This supervision guides the training of our GNNs (GCN, GAT, GraphSAGE, and GIN) to learn embeddings that capture biologically meaningful signals for downstream tasks such as disease prediction.
 
 .. contents::
    :local:
@@ -11,8 +9,7 @@ This section provides an overview of **Graph Neural Networks (GNNs)** in BioNeur
 
 Graph Convolutional Network (GCN)
 ---------------------------------
-
-GCN layers apply a spectral-based convolution operator to aggregate neighbor information. The core update equation for a single GCN layer is:
+GCN layers apply a spectral-based convolution operator to aggregate information from a node’s neighbors. The core update equation for a single GCN layer is:
 
 .. math::
 
@@ -22,16 +19,14 @@ GCN layers apply a spectral-based convolution operator to aggregate neighbor inf
 where
 
 - :math:`X^{(l)}` is the node feature matrix at layer :math:`l`.
-- :math:`\widehat{A} = A + I` is the adjacency matrix with inserted self-loops.
+- :math:`\widehat{A} = A + I` is the adjacency matrix with self-loops.
 - :math:`\widehat{D}` is the diagonal degree matrix of :math:`\widehat{A}`.
 - :math:`W^{(l)}` denotes the trainable parameters at layer :math:`l`.
-- :math:`\mathrm{ReLU}` is the rectified linear unit activation function.
-
+- :math:`\mathrm{ReLU}` is the activation function.
 
 Graph Attention Network (GAT)
 -----------------------------
-
-GAT layers incorporate attention coefficients to weight the contribution of neighbors:
+GAT layers incorporate learned attention coefficients to weight the contributions of neighboring nodes:
 
 .. math::
 
@@ -43,13 +38,11 @@ where
 - :math:`\alpha_{ij}^{(l)}` is the learned attention coefficient for the edge between nodes :math:`i` and :math:`j`.
 - :math:`W^{(l)}` is a trainable linear transformation.
 - :math:`\mathrm{ELU}` is the exponential linear unit activation.
-- :math:`\mathcal{N}(i)` is the set of neighbors of node :math:`i`.
-
+- :math:`\mathcal{N}(i)` denotes the neighbors of node :math:`i`.
 
 GraphSAGE
 ---------
-
-GraphSAGE (SAmple and aggreGatE) updates each node by concatenating its own features with an aggregated summary of its neighbors. In a mean-aggregator setting:
+GraphSAGE (SAmple and aggreGatE) computes node embeddings by concatenating a node’s own features with an aggregated summary (e.g., mean) of its neighbors’ features:
 
 .. math::
 
@@ -61,17 +54,13 @@ GraphSAGE (SAmple and aggreGatE) updates each node by concatenating its own feat
 
 where
 
-- :math:`\mathcal{N}(i)` are neighbors of node :math:`i`.
 - :math:`\|` denotes vector concatenation.
-- :math:`W^{(l)}` is a trainable weight matrix at layer :math:`l`.
-- :math:`\sigma` is a nonlinear activation function, e.g. ReLU.
-
+- :math:`W^{(l)}` is a trainable weight matrix.
+- :math:`\sigma` is a nonlinear activation function (e.g., ReLU).
 
 Graph Isomorphism Network (GIN)
 -------------------------------
-
-GIN uses a sum-aggregator combined with a learnable :math:`\epsilon` parameter
-and an MLP to update node representations:
+GIN employs a sum-aggregator combined with a learnable :math:`\epsilon` parameter and a multi-layer perceptron (MLP) to update node representations:
 
 .. math::
 
@@ -80,86 +69,66 @@ and an MLP to update node representations:
 
 where
 
-- :math:`\epsilon^{(l)}` is a (learnable or fixed) parameter for each layer :math:`l`.
-- :math:`\mathrm{MLP}^{(l)}` is a multi-layer perceptron at layer :math:`l`.
-
-
-Task-Driven vs. Unsupervised GNNs
----------------------------------
-
-1. **Task-Driven**:
-
-   If each node (e.g., a gene or protein) has a **numeric value** (e.g., correlation with a disease phenotype), you can train a GNN to predict this value (with MSE loss). This aligns node embeddings with the target measure, grouping nodes that have similar relationships.
-
-   **Pre-Computed or Random Node Features**:
-
-      - You can correlate each node with clinical variables to build a rich feature vector, **or** simply initialize node features randomly.
-      - Even with random features, the GNN can still learn **the label** drives training via MSE on each node’s predicted value vs. its real label.
+- :math:`\epsilon^{(l)}` is a learnable (or fixed) parameter.
+- :math:`\mathrm{MLP}^{(l)}` denotes a multi-layer perceptron.
 
-2. **Unsupervised**:
+Task-Driven (Supervised/Semi-Supervised) GNNs
+---------------------------------------------
+In our work, the GNNs are primarily **task-driven**:
 
-   If no explicit node label is provided, the GNN can learn from the **graph structure itself**:
+- **Node Labeling via Phenotype Correlation:**  
+  For each node, we compute the Pearson correlation between the omics data and phenotype (or clinical) data. This correlation serves as the target label during training.
 
-      - **Intrinsic Objectives**:
+- **Supervised Training Objective:**  
+  The GNN is trained to predict these correlation values using a Mean Squared Error (MSE) loss. This strategy aligns node embeddings with biological signals relevant to the disease phenotype.
 
-         e.g., graph autoencoders or contrastive losses.
-
-      - **Random Features**:
-
-         Possibly leaving node features random or minimal.
-
-      - **Structure as Signal**:
-
-         Even in the absence of external labels, adjacency patterns guide the GNN to produce embeddings capturing local/global relationships.
-
-      - **Practical Implementation**:
-
-         - In some quick explorations, you might skip external labeling entirely.
-         - The GNN produces a representation that captures the topology.
-
-Overall, both approaches yield node embeddings that can be integrated into **subject-level** datasets or used for clustering or further analysis.
+- **Downstream Integration:**  
+  The learned node embeddings can be integrated into patient-level datasets for sample-level classification tasks. For example, **DPMON** (Disease Prediction using Multi-Omics Networks) leverages these embeddings in an end-to-end pipeline where the final objective is to classify disease outcomes.
 
 Key Insights into GNN Parameters and Outputs
 --------------------------------------------
+1. **Input Parameters:**
 
-1. **Input Parameters**:
+   - **Node Features Matrix:** Built by correlating omics data with clinical variables.
+   - **Edge Index:** Derived from the network’s adjacency matrix.
+   - **Target Labels:** Numeric values representing the correlation between omics features and phenotype data.
 
-   - **Node Features Matrix**: :math:`(N \times F)` shape. You can use pre-computed correlations or random initialization.
-   - **Edge Index**: :math:`(2 \times E)` shape, specifying edges.
-   - **Edge Attributes (Optional)**: Weighted edges, etc.
-   - **Target Labels (Optional)**: If **task-driven** (MSE regression or classification), each node has a numeric or categorical label.
+2. **Output Embeddings:**
 
-2. **Output Embeddings**:
+   - The penultimate layer of the GNN produces dense node embeddings that capture both local connectivity and supervised signals.
+   - These embeddings can be further reduced (e.g., via PCA or an Autoencoder) for visualization or integrated into subject-level data.
 
-   - Each node is represented as a dense vector in the hidden layers.
-   - Typically, a final linear may reduce to 1D if you do node-level MSE, but the **penultimate** layer is multi-dimensional.
-   - After training, you retrieve the multi-dimensional “node embeddings” if needed.
+Dimensionality Reduction: PCA vs. Autoencoders
+------------------------------------------------
+After training a GNN, the resulting node embeddings are typically high-dimensional. To integrate these embeddings into the original omics data—by reweighting each feature—a further reduction step is performed to obtain a single summary value per feature. BioNeuralNet supports two primary approaches for this reduction:
 
-3. **Dimensionality Reduction**:
+**Principal Component Analysis (PCA)**  
+PCA is a linear dimensionality reduction technique that computes orthogonal components capturing the maximum variance in the data. The first principal component (PC1) is often used as a concise summary of each feature's variation. PCA is:
 
-   - After generating embeddings, techniques like PCA can reduce dimensionality for simpler usage and improved interpretability.
+- **Deterministic and Fast:** A closed-form solution is computed from the covariance matrix.
+- **Simple and Interpretable:** The linear combination of the original variables is straightforward to understand.
+- **Limited to Linear Relationships:** It may not capture more complex, nonlinear structures in the data.
 
-How DPMON Uses GNNs Differently
--------------------------------
+**Autoencoders (AE)**  
+Autoencoders are neural network models designed to learn a compressed representation (latent code) through a bottleneck architecture. They use nonlinear activations (e.g., ReLU) to model complex relationships:
 
-**DPMON** (Disease Prediction using Multi-Omics Networks) is an **end-to-end** pipeline
-that uses GNN layers for **sample-level classification**, rather than node-level MSE:
+- **Nonlinear Transformation:** The encoder learns to capture intricate patterns that a linear method might miss.
+- **Learned Representations:** The latent code is obtained by minimizing a reconstruction loss, making it adaptive to the data.
+- **Flexible and Tunable:** Being neural network–based, autoencoders allow tuning of architecture parameters (e.g., number of layers, hidden dimensions, epochs, learning rate) to better capture the signal. In our framework, we highly recommend using autoencoders (i.e., setting `tune=True`) to leverage their enhanced expressivity for complex multi-omics data.
 
-1. **Local + Global Structure**:
-   - DPMON’s GNN still aggregates information per node. However, it doesn’t use numeric per-node labels in MSE.
-   - Instead, it extracts node embeddings to feed into an autoencoder/dimension-averaging pipeline.
+In practice, PCA offers simplicity and interpretability, whereas autoencoders may yield superior performance by capturing more nuanced nonlinear relationships. The choice depends on the complexity of your data and the computational resources available. Our recommendation is to enable tuning (using `tune=True`) to optimize the autoencoder parameters for your specific dataset.
 
-2. **Classification Head**:
-   - Ultimately, DPMON’s GNN embeddings are combined with patient-level omics data.
-   - A downstream neural network (e.g., a softmax layer) predicts the patient’s disease phenotype.
-   - This is a **sample-level** classification objective (e.g., CrossEntropyLoss on patient classes).
+How DPMON Uses GNNs Differently
+-------------------------------
+**DPMON** (Disease Prediction using Multi-Omics Networks) reuses the same GNN architectures but with a different objective:
 
-Hence, DPMON’s approach reuses the **same** GNN architectures (GCN, GAT, etc.) but with a **different final objective** (classification at sample level, instead of node-level MSE).
+- Instead of node-level MSE regression, DPMON aggregates node embeddings with patient-level omics data.
+- A downstream classification head (e.g., softmax layer with CrossEntropyLoss) is applied for sample-level disease prediction.
+- This end-to-end approach leverages both local (node-level) and global (patient-level) network information.
 
 Example Usage
 -------------
-
-Below is a **simplified** snippet showing a **task-driven** node-level approach, where each node is assigned a numeric correlation with disease severity. The GNN (choose GCN, GAT, SAGE, or GIN) tries to predict that correlation, producing a trained embedding:
+Below is a simplified example that demonstrates the task-driven approach—where node labels are derived from phenotype correlations and used to train the GNN:
 
 .. code-block:: python
 
@@ -178,8 +147,4 @@ Below is a **simplified** snippet showing a **task-driven** node-level approach,
    gnn.fit()
    node_embeds = gnn.embed()
 
-If **no** numeric label is used, you can rely on an **unsupervised** or random feature initialization. Even when we skip correlation-based features and MSE training, the GNN still provides embeddings reflecting the graph structure.
-
-For sample-level classification, see :ref:`dpmon`. DPMON integrates node embeddings into an end-to-end disease prediction pipeline, focusing on **patient** labels rather than node-level numeric regression.
-
 Return to :doc:`../index`
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 150ea24..277df6a 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,48 +1,78 @@
-.. BioNeuralNet documentation master file
-
-Welcome to BioNeuralNet Beta 0.1
+Welcome to BioNeuralNet Beta 0.2
 ================================
 
 .. image:: https://img.shields.io/badge/license-MIT-blue.svg
    :target: https://github.com/UCD-BDLab/BioNeuralNet/blob/main/LICENSE
+
 .. image:: https://img.shields.io/pypi/v/bioneuralnet
    :target: https://pypi.org/project/bioneuralnet/
+
 .. image:: https://img.shields.io/pypi/pyversions/bioneuralnet
    :target: https://pypi.org/project/bioneuralnet/
+
 .. image:: https://static.pepy.tech/badge/bioneuralnet
    :target: https://pepy.tech/project/bioneuralnet
+
 .. image:: https://img.shields.io/badge/GitHub-View%20Code-blue
    :target: https://github.com/UCD-BDLab/BioNeuralNet
 
 .. figure:: _static/LOGO_WB.png
    :align: center
-   :alt: bioneuralnet logo
+   :alt: BioNeuralNet Logo
+
+BioNeuralNet – Advanced Multi-Omics Integration with GNNs
+==========================================================
+
+.. note::
+
+   **This is a BETA version of BioNeuralNet.**  
+   It is under active development, and certain features may be incomplete or subject to change.  
+   Your feedback and bug reports are highly encouraged to help us refine and improve the tool.
 
+Installation
+------------
+To install BioNeuralNet, simply run:
 
-**Note:** This is a **beta version** of BioNeuralNet. It is under active development, and certain features
-may be incomplete or subject to change. Feedback and bug reports are highly encouraged to help us
-improve the tool.
+.. code-block:: bash
 
-BioNeuralNet is a Python-based software tool designed to streamline the integration of multi-omics
-data with **Graph Neural Network (GNN)** embeddings. It supports **graph clustering**, **subject representation**,
-and **disease prediction**, enabling advanced analyses of complex multi-omics networks.
+   pip install bioneuralnet
+
+For additional installation details, including **GPU acceleration for GNNs**, see :doc:`installation`.
+
+What is BioNeuralNet?
+---------------------
+BioNeuralNet is a **Python-based** framework designed to bridge the gap between **multi-omics data analysis** and **Graph Neural Networks (GNNs)**. By leveraging advanced techniques, it enables:
+
+- **Graph Clustering** – Identify biologically meaningful communities within omics networks.  
+- **GNN Embeddings** – Learn network-based feature representations from biological graphs.  
+- **Subject Representation** – Generate high-quality embeddings for individuals based on multi-omics profiles.  
+- **Disease Prediction** – Build predictive models using integrated multi-layer biological networks.
+
+Seamless Data Integration
+-------------------------
+One of BioNeuralNet’s core strengths is **interoperability**:
+
+- Outputs are structured as **pandas DataFrames**, allowing easy downstream analysis.  
+- Supports integration with external tools, making it adaptable to various research workflows.  
+- Works effortlessly with network-based and machine learning pipelines.
+
+Designed for Researchers. Built for Innovation.
+-----------------------------------------------
+Start exploring BioNeuralNet today and push the boundaries of **multi-omics analysis**!
 
-**Python Installation via pip**:
 
    .. code-block:: bash
 
-      pip install bioneuralnet==0.1.0b1
+      pip install bioneuralnet
 
 For additional installation details, including GPU usage for GNNs, see :doc:`installation`.
 
 .. note::
-
    **External Tools**:
 
-   - We offer a number of external tools available through the `bioneuralnet.external_tools` module.
-   - These tools were implemented to facilitate testing, and should not be considered part of the package's core functionality.
-   - The classes inside the `external_tools` module are lightweight wrappers around existing tools and libraries offering minimal functionality.
-   - We highly encourage users to explore these tools outside of BioNeuralNet to fully leverage their capabilities.
+   - BioNeuralNet offers external tools available through the `bioneuralnet.external_tools` module.
+   - These lightweight wrappers (e.g., for WGCNA, SmCCNet, Node2Vec) facilitate testing and integration, but they are not mandatory to use the core functionality.
+   - We highly encourage users to explore these tools to fully leverage their capabilities.
 
 **Example: Transforming Multi-Omics for Enhanced Disease Prediction**
 ---------------------------------------------------------------------
@@ -57,28 +87,19 @@ For additional installation details, including GPU usage for GNNs, see :doc:`ins
 
 Below is a quick example demonstrating the following steps:
 
-1. **Building or Importing a Network Adjacency Matrix**:
-
-   - For instance, using external tools like **SmCCNet**.
-
-2. **Using DPMON for Disease Prediction**:
-
-   - A detailed explanation follows.
-
-**Steps:**
-
 1. **Data Preparation**:
 
    - Input your multi-omics data (e.g., proteomics, metabolomics) along with phenotype and clinical data.
 
 2. **Network Construction**:
 
-   - **Not performed internally**: You need to generate the adjacency matrix externally, using tools like **SmCCNet**.
-   - Lightweight wrappers are available in `bioneuralnet.external_tools` (e.g., WGCNA, SmCCNet) for convenience.
+   - **Not performed internally**: Generate the network adjacency matrix externally (e.g., using SmCCNet).
+   - Lightweight wrappers (e.g., WGCNA, SmCCNet) are available in `bioneuralnet.external_tools` for convenience.
 
 3. **Disease Prediction**:
-   - Use **DPMON** to predict disease phenotypes using the network information and omics data.
 
+   - Use **DPMON** to predict disease phenotypes by integrating the network information with omics data.
+   - DPMON supports an end-to-end pipeline with hyperparameter tuning that can return predictions as pandas DataFrames, enabling seamless integration with existing workflows.
 
 **Code Example**:
 
@@ -105,7 +126,7 @@ Below is a quick example demonstrating the following steps:
    adjacency_matrix = smccnet.run()
    print("Adjacency matrix generated.")
 
-   # Step 3: Disease Prediction
+   # Step 3: Disease Prediction (DPMON)
    dpmon = DPMON(
        adjacency_matrix=adjacency_matrix,
        omics_list=[omics_proteins, omics_metabolites],
@@ -117,14 +138,6 @@ Below is a quick example demonstrating the following steps:
    print("Disease phenotype predictions:\n", predictions)
 
 
-**Output**:
-  - **Adjacency Matrix**: The multi-omics network representation.
-  - **Predictions**: Disease phenotype predictions for each sample.
-
-.. note::
-
-   - For a complete script of DPMON and subject representation example, see :doc:`tutorials`.
-
 **BioNeuralNet Overview: Multi-Omics Integration with Graph Neural Networks**
 -----------------------------------------------------------------------------
 
@@ -133,52 +146,51 @@ BioNeuralNet offers five core steps in a typical workflow:
 1. **Graph Construction**:
 
    - **Not** performed internally. You provide or build adjacency matrices externally (e.g., via WGCNA, SmCCNet, or your own scripts).
-   - Lightweight wrappers are available in `bioneuralnet.external_tools` (e.g., WGCNA, SmCCNet) for convenience, but they are **not** mandatory for BioNeuralNet’s pipeline.
+   - All modules are designed to integrate seamlessly with pandas—most functions offer options to return results as pandas DataFrames, enabling you to incorporate BioNeuralNet outputs directly into your existing workflows.
 
 2. **Graph Clustering**:
 
-   - Identify functional modules or communities using `PageRank`.
-   - The `PageRank` module enables finding subnetwork clusters through personalized sweep cuts, capturing local neighborhoods influenced by seed nodes.
+   - Identify functional modules or communities using **correlated clustering methods** (e.g., CorrelatedPageRank, CorrelatedLouvain, HybridLouvain) that integrate phenotype correlation to extract biologically relevant modules [1]_.
+   - Clustering modules can return either raw partitions or induced subnetwork adjacency matrices (as DataFrames) for visualization.
 
 3. **Network Embedding**:
 
-   - Generate embeddings with methods like **GCN**, **GAT**, **GraphSAGE**, and **GIN**.
-   - You can attach numeric labels to nodes or remain "unsupervised," relying solely on graph structure and node features (e.g., correlation with clinical data).
+   - Generate embeddings using methods such as **GCN**, **GAT**, **GraphSAGE**, and **GIN**.
+   - Outputs can be obtained as native tensors or converted to pandas DataFrames for easy analysis and visualization.
 
 4. **Subject Representation**:
 
-   - Integrate node embeddings back into omics data to enrich each subject’s feature vector by weighting columns with the learned embedding scalars.
-   - For a complete example, see `examples/example_1.py` in the repository.
-5. **Downstream Tasks**:
-
-   - Perform advanced analyses such as disease prediction via **DPMON**, which trains a GNN end-to-end alongside a classifier, incorporating both local and global network information.
+   - Integrate node embeddings back into omics data to enrich subject-level profiles by weighting features with learned embedding scalars.
+   - The result can be returned as a DataFrame or a tensor, fitting naturally into downstream analyses.
 
-`View full-size image: BioNeuralNet Overview <https://bioneuralnet.readthedocs.io/en/latest/_images/BioNeuralNet.png>`_
-
-.. figure:: _static/BioNeuralNet.png
-   :align: center
-   :alt: BioNeuralNet
+5. **Downstream Tasks**:
 
-   BioNeuralNet Overview
+   - Execute end-to-end pipelines for disease prediction using **DPMON** [2]_.
+   - DPMON supports hyperparameter tuning—when enabled, it finds the best configuration and then performs standard training to produce final predictions as a pandas DataFrame.
+   - This approach, along with the native pandas integration across modules, ensures that BioNeuralNet can be easily incorporated into your analysis workflows.
 
 
 .. toctree::
    :maxdepth: 2
-   :caption: Documentation Contents:
+   :caption: Contents:
 
    installation
+   TOPMED.ipynb
+   gnns
+   clustering
    tutorials/index
    tools/index
    external_tools/index
-   gnns
+   future
    user_api
    faq
-   clustering
 
 
-Indices and tables
-==================
-
+Indices and References
+======================
 * :ref:`genindex`
 * :ref:`modindex`
 * :ref:`search`
+
+.. [1] Abdel-Hafiz, M., Najafi, M., et al. "Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification." *Frontiers in Big Data*, 5 (2022). DOI: `10.3389/fdata.2022.894632 <https://doi.org/10.3389/fdata.2022.894632>`_.
+.. [2] Hussein, S., Ramos, V., et al. "Learning from Multi-Omics Networks to Enhance Disease Prediction: An Optimized Network Embedding and Fusion Approach." In *2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)*, Lisbon, Portugal, 2024, pp. 4371-4378. DOI: `10.1109/BIBM62325.2024.10822233 <https://doi.org/10.1109/BIBM62325.2024.10822233>`_.
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
index dd3dd44..040cf69 100644
--- a/docs/source/installation.rst
+++ b/docs/source/installation.rst
@@ -7,7 +7,7 @@ BioNeuralNet supports Python 3.10 and 3.11 in this beta release. Follow the step
 
    .. code-block:: bash
 
-      pip install bioneuralnet==0.1.0b1
+      pip install bioneuralnet
 
    This installs the core BioNeuralNet modules for GNN embeddings, subject representation,
    disease prediction (DPMON), and clustering.
@@ -41,6 +41,7 @@ BioNeuralNet supports Python 3.10 and 3.11 in this beta release. Follow the step
    If you plan to use **WGCNA** or **SmCCNet** for network construction:
 
    - Install R from `The R Project <https://www.r-project.org/>`_.
+      - Version 4.4.2 or higher is recommended.
    - Install the required R packages. Open R and run:
 
      .. code-block:: r
@@ -53,7 +54,7 @@ BioNeuralNet supports Python 3.10 and 3.11 in this beta release. Follow the step
 
 4. **Additional Notes for External Tools**:
 
-   For **Node2Vec**, **feature selection**, or **visualization modules**, refer to the :doc:`external_tools/index`.
+   Refer to the :doc:`external_tools/index`.
 
 5. **Next Steps**:
 
diff --git a/docs/source/tools/Graph_Clustering.rst b/docs/source/tools/Graph_Clustering.rst
index b57f364..787b517 100644
--- a/docs/source/tools/Graph_Clustering.rst
+++ b/docs/source/tools/Graph_Clustering.rst
@@ -1,14 +1,12 @@
 Graph Clustering
-================
+=================
 
-**Clustering (Hierarchical):**
+**Correlated clustering on complex networks:**
 
-.. literalinclude:: ../examples/hierarchical_example.py
+.. literalinclude:: ../examples/correlated_louvain_example.py
    :language: python
-   :caption: Example of Hierarchical Clustering
+   :caption: Example correlated Louvain clustering workflow
 
-**Clustering (PageRank):**
-
-.. literalinclude:: ../examples/pagerank_example.py
+.. literalinclude:: ../examples/correlated_hybrid_louvain.py
    :language: python
-   :caption: Example of PageRank Clustering
+   :caption: Example correlated hybrid Louvain clustering workflow
diff --git a/docs/source/tutorials/example_1.rst b/docs/source/tutorials/example_1.rst
index 49eb9f4..985db08 100644
--- a/docs/source/tutorials/example_1.rst
+++ b/docs/source/tutorials/example_1.rst
@@ -1,114 +1,103 @@
 Example 1: SmCCNet + GNN Embeddings + Subject Representation
 ============================================================
+
 This tutorial illustrates how to:
 
-1. **Build:** an adjacency matrix with SmCCNet (external).
-2. **Enhance Representation:** Enhance the representation of subjects using GNN embeddings.
+1. **Build**: an adjacency matrix with SmCCNet.
+2. **Enhance Representation**: Generate node embeddings using GNNEmbedding.
+3. **Integrate**: Incorporate these embeddings into subject-level omics data using GraphEmbedding.
 
 **Workflow**:
 
 1. **Construct**:
-   - A multi-omics network adjacency using SmCCNet (an external R-based tool).
+   - A multi-omics network adjacency using SmCCNet.
 2. **Generate**:
    - Node embeddings with a Graph Neural Network (GNN).
-3. **Integrate** :
-   - Those embeddings into subject-level omics data for enhanced representation.
-4. **Diagram of the workflow**:
-   - The figure below illustrates the process.
-
-`View full-size image: Subject Representation <https://bioneuralnet.readthedocs.io/en/latest/_images/SubjectRepresentation.png>`_
+3. **Integrate**:
+   - These embeddings into subject-level omics data for enhanced representation.
 
-.. figure:: _static/SubjectRepresentation.png
+.. figure:: ../_static/SubjectRepresentation.png
    :align: center
    :alt: Subject Representation Workflow
 
    Subject-level embeddings provide richer phenotypic and clinical context.
 
+`View full-size image: Subject Representation <https://bioneuralnet.readthedocs.io/en/latest/_images/SubjectRepresentation.png>`_
 
-**Step-by-Step**:
+**Step-by-Step Instructions**:
 
 1. **Data Setup**:
-   - Omics data, phenotype data, clinical data as Pandas DataFrames or Series.
+   - Load omics data, phenotype data, and clinical data using DatasetLoader.
 
-2. **Network Construction** (SmCCNet):
-   - We call `SmCCNet.run()` to produce an adjacency matrix from the multi-omics data.
+2. **Network Construction (SmCCNet)**:
+   - Call `SmCCNet.run()` to produce an adjacency matrix from multi-omics data.
 
 3. **GNN Embedding**:
-   - We pass the adjacency, omics data, and (optionally) clinical data to `GNNEmbedding`.
-   - GNNEmbedding’s `.run()` yields node embeddings.
+   - Pass the adjacency, omics data, and (optionally) clinical data to `GNNEmbedding`.
+   - Use `.fit()` and `.embed()` to generate node embeddings.
 
 4. **Subject Representation**:
-   - We can integrate these embeddings back into omics data via `GraphEmbedding`.
+   - Integrate these embeddings into omics data via `GraphEmbedding`.
 
 .. note::
    For a **complete** script, see `examples/example_1.py` in the repository.
 
-Below is a **complete** snippet:
+Below is a **complete** Python implementation:
 
 .. code-block:: python
 
    import pandas as pd
+   from bioneuralnet.datasets import DatasetLoader
    from bioneuralnet.external_tools import SmCCNet
    from bioneuralnet.network_embedding import GNNEmbedding
    from bioneuralnet.subject_representation import GraphEmbedding
 
-   # 1) Prepare data
-   omics_data = pd.read_csv("data/omics_data.csv")
-   phenotype_data = pd.read_csv("data/phenotype_data.csv")
-   clinical_data = pd.read_csv("data/clinical_data.csv")
+   # 1) Load dataset
+   loader = DatasetLoader("example1")
+   omics1, omics2, phenotype, clinical = loader.load_data()
+
+   # 2) Merge omics data
+   merged_omics = pd.concat([omics1, omics2], axis=1)
 
-   # 2) Run SmCCNet to get adjacency
+   # 3) Generate adjacency matrix using SmCCNet
    smccnet = SmCCNet(
-      phenotype_df=phenotype_data,
-      omics_df=omics_data,
-      data_types=["genes, proteins"]
-      kfolds=5,
-      summarization = "NetSHy",
-      seed: 127,
-      )
-   adjacency_matrix = smccnet.run()
-
-   # 3) Generate embeddings
+       phenotype_df=phenotype,
+       omics_dfs=[omics1, omics2],
+       data_types=["genes", "proteins"],
+       kfold=3,
+       subSampNum=500,
+   )
+   global_network, smccnet_clusters = smccnet.run()
+
+   # 4) Generate embeddings using GNNEmbedding
    gnn_embedding = GNNEmbedding(
-       adjacency_matrix=adjacency_matrix,
-       omics_data=omics_data,
-       phenotype_data=phenotype_data,
-       clinical_data=clinical_data,
-       phenotype_col = "phenotype",
-       model_type='GAT',
-       hidden_dim=32,
-       layer_num = 4,
-       dropout=True,
-       num_epochs=20,
-       lr=1e-3,
-       weight_decay=1e-4,
+       adjacency_matrix=global_network,
+       omics_data=merged_omics,
+       phenotype_data=phenotype,
+       clinical_data=clinical,
+       tune=True,
    )
    gnn_embedding.fit()
-   embeddings_tensor = gnn_embedding.embed()
-
-   node_names = adjacency_matrix.columns.tolist()
-   embeddings_df = pd.DataFrame(embeddings_tensor.numpy(), index=node_names)
-   print("GNN embeddings generated. Shape:", embeddings_df.shape)
-
-   # 4) Subject-level representation:
-   graph_embed = GraphEmbedding(
-       adjacency_matrix=adjacency_matrix,
-       omics_data=omics_data,
-       phenotype_data=phenotype_data,
-       clinical_data=clinical_data,
-       embeddings = node_embeddings_df,
-       reduce_method = "PCA"
+   embeddings_output = gnn_embedding.embed(as_df=True)
+
+   print(f"GNN embeddings generated. Shape: {embeddings_output.shape}")
+
+   # 5) Perform subject representation using GraphEmbedding
+   graph_embedding = GraphEmbedding(
+       omics_data=merged_omics,
+       embeddings=embeddings_output,
+       phenotype_data=phenotype,
+       tune=True,
    )
-   #Other options to reduce embeddings inlcude "PCA", "avg" and "max".
-   enhanced_data = graph_embed.run()
-   print("Enhanced omics data shape:", enhanced_data.shape)
-   print("Enhanced omics data columns:", enhanced_data.head())
 
-   #saving your enhanced data to a csv file
+   enhanced_data = graph_embedding.run()
+   print(f"Enhanced omics data shape: {enhanced_data.shape}")
+
+   # Save enhanced omics data
    enhanced_data.to_csv("enhanced_omics_data.csv")
 
 **Results**:
 
-   - **Adjacency Matrix** from SmCCNet
-   - **Node Embeddings** from GNN
-   - **Enhanced Omics Data**, integrating node embeddings for subject-level analysis
+- **Adjacency Matrix** generated using SmCCNet.
+- **Node Embeddings** from GNN.
+- **Enhanced Omics Data**, integrating node embeddings for subject-level analysis.
diff --git a/docs/source/tutorials/example_2.rst b/docs/source/tutorials/example_2.rst
index 86a9b29..f5995ec 100644
--- a/docs/source/tutorials/example_2.rst
+++ b/docs/source/tutorials/example_2.rst
@@ -3,73 +3,81 @@ Example 2: SmCCNet + DPMON for Disease Prediction
 
 This tutorial illustrates how to:
 
-1. **Build** an adjacency matrix with SmCCNet (external).
+1. **Build** an adjacency matrix with SmCCNet.
 2. **Predict** disease phenotypes using DPMON.
 
 **Workflow**:
 
 1. **Data Preparation**:
-   - Multi-omics data, phenotype data with disease labels, and (optionally) clinical data.
+   - Load multi-omics, phenotype, and clinical data using DatasetLoader.
 
 2. **Network Construction**:
-   - Use `SmCCNet` to create an adjacency matrix from the combined omics data.
+   - Use `SmCCNet.run()` to create an adjacency matrix from the combined omics data.
 
 3. **Disease Prediction**:
-   - `DPMON` integrates the adjacency matrix, omics data, and phenotype to train a GNN + classifier end-to-end.
+   - `DPMON` integrates the adjacency matrix, omics data, and phenotype data to train a GNN-based classifier.
 
-4. **Diagram of the workflow**: The figure below illustrates the process.
-
-`View full-size image: Disease Prediction (DPMON) <https://bioneuralnet.readthedocs.io/en/latest/_images/DPMON.png>`_
-
-.. figure:: _static/DPMON.png
+.. figure:: ../_static/DPMON.png
    :align: center
    :alt: Disease Prediction (DPMON)
 
    Embedding-enhanced subject data using DPMON for improved disease prediction.
 
+`View full-size image: Disease Prediction (DPMON) <https://bioneuralnet.readthedocs.io/en/latest/_images/DPMON.png>`_
+
+**Step-by-Step Instructions**:
+
+1. **Data Setup**:
+   - Load synthetic multi-omics, phenotype, and clinical data using `DatasetLoader`.
+
+2. **Network Construction (SmCCNet)**:
+   - Call `SmCCNet.run()` to produce an adjacency matrix from the omics data.
+
+3. **Disease Prediction (DPMON)**:
+   - Pass the adjacency, omics, phenotype, and clinical data into `DPMON`.
+   - Run `.run()` to predict disease phenotypes.
+
+Below is a **complete** Python implementation:
+
 .. code-block:: python
 
+   import pandas as pd
+   from bioneuralnet.datasets import DatasetLoader
    from bioneuralnet.external_tools import SmCCNet
    from bioneuralnet.downstream_task import DPMON
-   import pandas as pd
 
-   # 1) Prepare data
-   omics_data = pd.read_csv("data/omics_data.csv")
-   phenotype_data = pd.read_csv("data/phenotype_data.csv")
-   clinical_data = pd.read_csv("data/clinical_data.csv")
+   # 1) Load dataset
+   loader = DatasetLoader("example1")
+   omics1, omics2, phenotype, clinical = loader.load_data()
+
+   # 2) Merge omics data
+   merged_omics = pd.concat([omics1, omics2], axis=1)
 
-   # 2) Run SmCCNet to get adjacency
+   # 3) Generate adjacency matrix using SmCCNet
    smccnet = SmCCNet(
-      phenotype_df=phenotype_data,
-      omics_df=omics_data,
-      data_types=["genes, proteins"]
-      kfolds=5,
-      summarization = "NetSHy",
-      seed: 127,
-      )
-   adjacency_matrix = smccnet.run()
-
-   # 3) Disease Prediction with DPMON
+       phenotype_df=phenotype,
+       omics_dfs=[omics1, omics2],
+       data_types=["genes", "proteins"],
+       kfold=3,
+       subSampNum=500,
+   )
+   global_network, smccnet_clusters = smccnet.run()
+
+   print("Adjacency matrix generated using SmCCNet.")
+
+   # 4) Run Disease Prediction using DPMON
    dpmon = DPMON(
-      adjacency_matrix=adjacency_matrix,
-      omics_list=[omics_data],
-      phenotype_data=phenotype_data,
-      clinical_data=clinical_data,
-      model: "GAT",
-      gnn_hidden_dim: 64,
-      layer_num: 3,
-      nn_hidden_dim1: 2,
-      nn_hidden_dim2: 2,
-      epoch_num: 10,
-      repeat_num: 5,
-      lr: 0.01,
-      weight_decay: 1e-4,
-      tune: True,
-      gpu: False
+       adjacency_matrix=global_network,
+       omics_list=[omics1, omics2],
+       phenotype_data=phenotype,
+       clinical_data=clinical,
+       tune=True,
    )
-   predictions = dpmon.run()
-   print("Disease predictions:\n", predictions)
+   dpmon_predictions = dpmon.run()
+
+   print("\nDPMON Predictions:")
+   print(dpmon_predictions.head())
 
 **Output**:
-- **Adjacency Matrix**: from SmCCNet
-- **Predictions**: Phenotype predictions for each subject
+- **Adjacency Matrix**: Generated using SmCCNet.
+- **Predictions**: Phenotype predictions for each subject.
diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst
index 3b676e2..495479a 100644
--- a/docs/source/tutorials/index.rst
+++ b/docs/source/tutorials/index.rst
@@ -4,7 +4,7 @@ Tutorials
 These tutorials illustrate how to combine BioNeuralNet components for a cohesive multi-omics analysis.
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
 
    example_1
    example_2
diff --git a/docs/source/user_api.rst b/docs/source/user_api.rst
index ac9701a..195815e 100644
--- a/docs/source/user_api.rst
+++ b/docs/source/user_api.rst
@@ -9,9 +9,12 @@ The **User API** lists BioNeuralNet’s key classes, methods, and utilities.
 
    bioneuralnet.external_tools
    bioneuralnet.network_embedding
-   bioneuralnet.downstream_task
+   bioneuralnet.clustering
    bioneuralnet.subject_representation
+   bioneuralnet.downstream_task
    bioneuralnet.utils
+   bioneuralnet.datasets
+   bioneuralnet.metrics
 
 Executables
 -----------
@@ -19,8 +22,8 @@ Executables
 Certain classes expose a high-level ``run()`` method to perform end-to-end workflows:
 
 - **SmCCNet** or **WGCNA** (from `bioneuralnet.external_tools`) for adjacency generation
-- **GNNEmbedding** or **Node2Vec** for embeddings
 - **GraphEmbedding** for integrating embeddings into subject-level data
+- **CorrelatedLouvain** or **HybridLouvain** for clustering
 - **DPMON** for disease prediction
 
 **Usage Pattern**:
@@ -43,7 +46,6 @@ Example:
    )
 
    predictions = dpmon_obj.run()
-   print(predictions.head())
 
 **Methods**:
 
@@ -52,10 +54,13 @@ Below are references to the ``run()`` methods:
 .. automethod:: bioneuralnet.external_tools.smccnet.SmCCNet.run
    :no-index:
 
-.. automethod:: bioneuralnet.network_embedding.gnn_embedding.GNNEmbedding.run
+.. automethod:: bioneuralnet.subject_representation.GraphEmbedding.run
+   :no-index:
+
+.. automethod:: bioneuralnet.clustering.CorrelatedLouvain.run
    :no-index:
 
-.. automethod:: bioneuralnet.subject_representation.GraphEmbedding.run
+.. automethod:: bioneuralnet.clustering.HybridLouvain.run
    :no-index:
 
 .. automethod:: bioneuralnet.downstream_task.DPMON.run
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 04ed080..3069b6a 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,5 +1,3 @@
-# Development dependencies
-
 # testing
 pytest>=6.0
 pytest-cov
@@ -10,6 +8,10 @@ sphinx
 sphinx_rtd_theme
 sphinx-autosummary-accessors
 sphinxcontrib-napoleon
+nbsphinx
+pandoc
+nbconvert
+ipykernel
 
 # code quality and formatting
 flake8
diff --git a/requirements.txt b/requirements.txt
index 995232c..e391f11 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,18 +1,21 @@
-# requirements.in
 wheel
 setuptools
 PyYAML
 pandas
 numpy
+scipy
 scikit-learn
 node2vec
 matplotlib
 ray[tune]
 tensorboardX
 networkx
-pyvis
-leidenalg
 dtt
-pyreadr
+cptac
+python-louvain
+debugpy
+seaborn
+umap-learn
+ipywidgets 
 # torch
 # torch_geometric
diff --git a/setup.cfg b/setup.cfg
index e5c432e..1f451a3 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = bioneuralnet
-version = 0.1.0b1
+version = 0.2.0b2
 author = Vicente Ramos
 author_email = vicente.ramos@ucdenver.edu
 description = A comprehensive framework for integrating multi-omics data with neural network embeddings.
@@ -30,10 +30,13 @@ install_requires =
     ray[tune]>=2.0
     tensorboardX>=2.5
     networkx>=2.5
-    pyvis>=0.2
-    leidenalg>=0.8
     dtt>=0.9.0
-    pyreadr>=0.4
+    scipy
+    cptac
+    python-louvain
+    debugpy
+    umap-learn
+    ipywidgets 
 
 [options.extras_require]
 dev =
@@ -44,9 +47,11 @@ dev =
     sphinx_rtd_theme
     sphinx-autosummary-accessors
     sphinxcontrib-napoleon
+    nbsphinx
     flake8
     black
     mypy
+    git-filter-repo
     pre-commit
     tox
     setuptools>=42
@@ -61,6 +66,3 @@ exclude =
     docs
     examples
 
-[options.entry_points]
-# console_scripts =
-#     bioneuralnet=bioneuralnet.__main__:main
diff --git a/tests/test_dataset_dpmon.py b/tests/test_dataset_dpmon.py
deleted file mode 100644
index fc7924a..0000000
--- a/tests/test_dataset_dpmon.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import unittest
-import pandas as pd
-
-from bioneuralnet.external_tools import SmCCNet
-from bioneuralnet.datasets import DatasetLoader
-from bioneuralnet.downstream_task.dpmon import DPMON
-
-
-class TestDPMONRealData(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        """
-        Load sample dataset and generate adjacency with SmCCNet
-        """
-        cls.datasets = DatasetLoader()
-        cls.omics1, cls.omics2, cls.pheno = cls.datasets("example1")
-
-        cls.omics_list = [cls.omics1, cls.omics2]
-        cls.phenotype_data = cls.pheno.rename(columns={"Pheno": "phenotype"})
-
-        cls.smccnet = SmCCNet(
-            phenotype_df=cls.phenotype_data,
-            omics_dfs=cls.omics_list,
-            data_types=["Genomics", "miRNA"],
-            kfold=5,
-            summarization="PCA",
-            seed=127,
-        )
-        cls.adjacency_matrix = cls.smccnet.run()
-
-    def test_dpmon_run(self):
-        """
-        Test DPMON on real adjacency + real multi-omics data
-        """
-
-        dpmon = DPMON(
-            adjacency_matrix=self.adjacency_matrix,
-            omics_list=self.omics_list,
-            phenotype_data=self.phenotype_data,
-            clinical_data=None,
-            model="GAT",
-            gnn_hidden_dim=16,
-            layer_num=2,
-            nn_hidden_dim1=8,
-            nn_hidden_dim2=8,
-            epoch_num=1,
-            repeat_num=1,
-            lr=0.01,
-            weight_decay=1e-4,
-            gpu=False,
-            tune=False,
-        )
-
-        predictions = dpmon.run()
-
-        self.assertIsInstance(predictions, pd.DataFrame)
-        if not predictions.empty:
-            self.assertIn("Actual", predictions.columns)
-            self.assertIn("Predicted", predictions.columns)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_dataset_gnn_embeddings.py b/tests/test_dataset_gnn_embeddings.py
deleted file mode 100644
index 65af4c6..0000000
--- a/tests/test_dataset_gnn_embeddings.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import unittest
-import torch
-import pandas as pd
-
-from bioneuralnet.external_tools import SmCCNet
-from bioneuralnet.datasets import DatasetLoader
-from bioneuralnet.network_embedding import GNNEmbedding
-
-
-class TestGNNEmbeddingRealData(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        """
-        Load sample dataset via DatasetLoader and generate adjacency with SmCCNet
-        """
-        cls.datasets = DatasetLoader()
-        cls.omics1, cls.omics2, cls.pheno = cls.datasets("example1")
-
-        cls.smccnet = SmCCNet(
-            phenotype_df=cls.pheno,
-            omics_dfs=[cls.omics1, cls.omics2],
-            data_types=["Genomics", "miRNA"],
-            kfold=5,
-            summarization="PCA",
-            seed=127,
-        )
-        cls.adjacency_matrix = cls.smccnet.run()
-        cls.omics = pd.concat([cls.omics1, cls.omics2], axis=1)
-
-        cls.pheno_data = cls.pheno.rename(columns={"Pheno": "phenotype"})
-
-    def test_gnn_embedding_run(self):
-        """
-        Test GNNEmbedding with real adjacency + multi-omics data to produce node embeddings.
-        """
-        gnn = GNNEmbedding(
-            adjacency_matrix=self.adjacency_matrix,
-            omics_data=self.omics,
-            phenotype_data=self.pheno_data,
-            clinical_data=None,
-            phenotype_col="phenotype",
-            model_type="GAT",
-            hidden_dim=32,
-            layer_num=2,
-            dropout=True,
-            num_epochs=1,
-            lr=1e-3,
-            weight_decay=1e-4,
-            gpu=False,
-        )
-
-        gnn.fit()
-        node_embeddings = gnn.embed()
-
-        self.assertIsInstance(
-            node_embeddings, torch.Tensor, "Embeddings must be a torch.Tensor"
-        )
-        self.assertEqual(
-            node_embeddings.shape[0],
-            self.adjacency_matrix.shape[0],
-            "Number of node embeddings must match adjacency dimension.",
-        )
-        self.assertGreater(
-            node_embeddings.shape[1],
-            1,
-            "Embeddings dimension should be >1 (multi-dimensional).",
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_dataset_subject_representation.py b/tests/test_dataset_subject_representation.py
deleted file mode 100644
index c0da2c0..0000000
--- a/tests/test_dataset_subject_representation.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import unittest
-import torch
-import pandas as pd
-
-from bioneuralnet.external_tools import SmCCNet
-from bioneuralnet.datasets import DatasetLoader
-from bioneuralnet.subject_representation import GraphEmbedding
-
-
-class TestSubjectRepresentationRealData(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        """
-        Load sample dataset via DatasetLoader and generate adjacency with SmCCNet
-        """
-        cls.datasets = DatasetLoader()
-        cls.omics1, cls.omics2, cls.pheno = cls.datasets("example1")
-        cls.omics = pd.concat([cls.omics1, cls.omics2], axis=1)
-        cls.pheno_data = cls.pheno.rename(columns={"Pheno": "phenotype"})
-
-        cls.smccnet = SmCCNet(
-            phenotype_df=cls.pheno_data,
-            omics_dfs=[cls.omics],
-            data_types=["Genomics+miRNA"],
-            kfold=5,
-            summarization="PCA",
-            seed=127,
-        )
-        cls.adjacency_matrix = cls.smccnet.run()
-
-    def test_graph_embedding_pipeline(self):
-        """
-        Test GraphEmbedding with real adjacency + real omics data (GNN-based approach).
-        """
-        graph_embed = GraphEmbedding(
-            adjacency_matrix=self.adjacency_matrix,
-            omics_data=self.omics,
-            phenotype_data=self.pheno_data,
-            clinical_data=None,
-            embeddings=None,
-            reduce_method="PCA",
-        )
-
-        enhanced_omics_data = graph_embed.run()
-
-        self.assertIsInstance(enhanced_omics_data, pd.DataFrame)
-        self.assertFalse(
-            enhanced_omics_data.empty, "Enhanced omics data should not be empty."
-        )
-
-        self.assertEqual(
-            enhanced_omics_data.shape[0],
-            self.omics.shape[0],
-            "Enhanced data must have same #samples (rows) as original omics.",
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_dynamic_visualization.py b/tests/test_dynamic_visualization.py
deleted file mode 100644
index 276f955..0000000
--- a/tests/test_dynamic_visualization.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import pytest
-import pandas as pd
-import networkx as nx
-from bioneuralnet.external_tools import DynamicVisualizer
-from unittest import mock
-
-
-@pytest.fixture
-def sample_adjacency_matrix(tmp_path):
-    """
-    Creates a sample adjacency matrix for testing.
-    """
-    data = {"gene1": [0, 1, 0], "gene2": [1, 0, 1], "gene3": [0, 1, 0]}
-    adjacency_matrix = pd.DataFrame(data, index=["gene1", "gene2", "gene3"])
-    return adjacency_matrix
-
-
-@pytest.fixture
-def temp_output_dir(tmp_path):
-    """
-    Provides a temporary directory for output files.
-    """
-    return tmp_path / "output"
-
-
-def test_generate_graph(sample_adjacency_matrix):
-    """
-    Test that DynamicVisualizer generates the correct NetworkX graph from the adjacency matrix.
-    """
-    visualizer = DynamicVisualizer(adjacency_matrix=sample_adjacency_matrix)
-    G = visualizer.generate_graph()
-
-    assert isinstance(G, nx.Graph)
-    assert G.number_of_nodes() == 3
-    assert G.number_of_edges() == 2
-    assert "gene1" in G
-    assert "gene2" in G
-    assert "gene3" in G
-    assert G.has_edge("gene1", "gene2")
-    assert G.has_edge("gene2", "gene3")
-    assert not G.has_edge("gene1", "gene3")
-
-
-def test_visualize_creates_file(sample_adjacency_matrix, temp_output_dir):
-    """
-    Test that the visualize method creates the expected HTML file.
-    """
-    visualizer = DynamicVisualizer(
-        adjacency_matrix=sample_adjacency_matrix,
-        output_dir=temp_output_dir,
-        output_filename="test_dynamic_network.html",
-    )
-
-    with mock.patch(
-        "bioneuralnet.external_tools.dynamic_visualization.get_logger"
-    ) as mock_get_logger:
-        mock_logger = mock.Mock()
-        mock_get_logger.return_value = mock_logger
-        G = visualizer.generate_graph()
-        visualizer.visualize(G)
-
-    output_file = temp_output_dir / "test_dynamic_network.html"
-    assert output_file.exists()
-    assert output_file.is_file()
-
-
-def test_visualize_with_invalid_layout(sample_adjacency_matrix, temp_output_dir):
-    """
-    Test that the visualize method handles invalid layout options gracefully.
-    """
-    invalid_layout = "invalid_layout"
-    visualizer = DynamicVisualizer(
-        adjacency_matrix=sample_adjacency_matrix,
-        layout=invalid_layout,
-        output_dir=temp_output_dir,
-        output_filename="dynamic_network_invalid.html",
-    )
-
-    with mock.patch.object(visualizer.logger, "warning") as mock_warning:
-        G = visualizer.generate_graph()
-        visualizer.visualize(G)
-
-        output_file = temp_output_dir / "dynamic_network_invalid.html"
-        assert output_file.exists()
-        assert output_file.is_file()
-
-        mock_warning.assert_called_with(
-            f"Layout '{invalid_layout}' not recognized. Falling back to spring layout."
-        )
-
-
-def test_visualize_with_empty_adjacency_matrix(tmp_path):
-    """
-    Test that the visualize method handles an empty adjacency matrix.
-    """
-    empty_adjacency = pd.DataFrame()
-    visualizer = DynamicVisualizer(
-        adjacency_matrix=empty_adjacency,
-        output_dir=tmp_path,
-        output_filename="dynamic_network_empty.html",
-    )
-
-    with pytest.raises(nx.NetworkXError):
-        G = visualizer.generate_graph()
-        visualizer.visualize(G)
-
-
-def test_visualize_output_directory_creation(sample_adjacency_matrix, tmp_path):
-    """
-    Test that the visualize method creates the output directory if it does not exist.
-    """
-    non_existent_dir = tmp_path / "non_existent_output"
-    visualizer = DynamicVisualizer(
-        adjacency_matrix=sample_adjacency_matrix,
-        output_dir=non_existent_dir,
-        output_filename="dynamic_network_new_dir.html",
-    )
-
-    with mock.patch(
-        "bioneuralnet.external_tools.dynamic_visualization.get_logger"
-    ) as mock_get_logger:
-        mock_logger = mock.Mock()
-        mock_get_logger.return_value = mock_logger
-        G = visualizer.generate_graph()
-        visualizer.visualize(G)
-
-    output_file = non_existent_dir / "dynamic_network_new_dir.html"
-    assert output_file.exists()
-    assert output_file.is_file()
-
-
-def test_visualize_with_custom_visualization_params(
-    sample_adjacency_matrix, temp_output_dir
-):
-    """
-    Test that custom visualization parameters are applied correctly.
-    """
-    visualizer = DynamicVisualizer(
-        adjacency_matrix=sample_adjacency_matrix,
-        layout="hierarchical",
-        bgcolor="#f0f0f0",
-        font_color="blue",
-        output_dir=temp_output_dir,
-        output_filename="dynamic_network_custom.html",
-        width="80%",
-        height="600px",
-    )
-
-    with mock.patch(
-        "bioneuralnet.external_tools.dynamic_visualization.get_logger"
-    ) as mock_get_logger:
-        mock_logger = mock.Mock()
-        mock_get_logger.return_value = mock_logger
-        G = visualizer.generate_graph()
-        visualizer.visualize(G)
-
-    output_file = temp_output_dir / "dynamic_network_custom.html"
-    assert output_file.exists()
-    assert output_file.is_file()
diff --git a/tests/test_feature_selector.py b/tests/test_feature_selector.py
deleted file mode 100644
index 37c24c9..0000000
--- a/tests/test_feature_selector.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import os
-import unittest
-import pandas as pd
-from bioneuralnet.external_tools import FeatureSelector
-
-
-class TestFeatureSelector(unittest.TestCase):
-
-    def setUp(self):
-        self.enhanced_omics_data = pd.DataFrame(
-            {
-                "feature1": [0.1, 0.2, 0.3, 0.4, 0.5],
-                "feature2": [1.2, 1.3, 1.1, 1.4, 1.5],
-                "feature3": [2.1, 2.2, 2.3, 2.4, 2.5],
-                "feature4": [3.1, 3.2, 3.3, 3.4, 3.5],
-                "feature5": [4.1, 4.2, 4.3, 4.4, 4.5],
-                "feature6": [5.1, 5.2, 5.3, 5.4, 5.5],
-                "feature7": [6.1, 6.2, 6.3, 6.4, 6.5],
-                "feature8": [7.1, 7.2, 7.3, 7.4, 7.5],
-                "feature9": [8.1, 8.2, 8.3, 8.4, 8.5],
-                "feature10": [9.1, 9.2, 9.3, 9.4, 9.5],
-                "feature11": [10.1, 10.2, 10.3, 10.4, 10.5],
-                "feature12": [11.1, 11.2, 11.3, 11.4, 11.5],
-                "feature13": [12.1, 12.2, 12.3, 12.4, 12.5],
-                "feature14": [13.1, 13.2, 13.3, 13.4, 13.5],
-                "feature15": [14.1, 14.2, 14.3, 14.4, 14.5],
-            }
-        )
-
-        self.phenotype_data = pd.Series([0, 1, 0, 1, 0], name="Phenotype")
-
-    def test_initialization(self):
-        """
-        Test the initialization of the FeatureSelector class.
-        """
-        fs = FeatureSelector(
-            enhanced_omics_data=self.enhanced_omics_data,
-            phenotype_data=self.phenotype_data,
-            num_features=5,
-            selection_method="correlation",
-        )
-        self.assertEqual(fs.num_features, 5)
-        self.assertEqual(fs.selection_method, "correlation")
-        self.assertIsInstance(fs.enhanced_omics_data, pd.DataFrame)
-        self.assertIsInstance(fs.phenotype_data, pd.Series)
-        self.assertTrue(hasattr(fs, "logger"))
-
-    def test_correlation_selection(self):
-        """
-        Test correlation-based feature selection.
-        """
-        fs = FeatureSelector(
-            enhanced_omics_data=self.enhanced_omics_data,
-            phenotype_data=self.phenotype_data,
-            num_features=5,
-            selection_method="correlation",
-        )
-        selected = fs.perform_feature_selection()
-        self.assertEqual(selected.shape[1], 5)
-
-    def test_lasso_selection(self):
-        """
-        Test LASSO-based feature selection.
-        """
-        fs = FeatureSelector(
-            enhanced_omics_data=self.enhanced_omics_data,
-            phenotype_data=self.phenotype_data,
-            num_features=5,
-            selection_method="lasso",
-        )
-        selected = fs.perform_feature_selection()
-        self.assertEqual(selected.shape[1], 5)
-
-    def test_random_forest_selection(self):
-        """
-        Test Random Forest-based feature selection.
-        """
-        fs = FeatureSelector(
-            enhanced_omics_data=self.enhanced_omics_data,
-            phenotype_data=self.phenotype_data,
-            num_features=5,
-            selection_method="random_forest",
-        )
-        selected = fs.perform_feature_selection()
-        self.assertEqual(selected.shape[1], 5)
-
-    def test_invalid_selection_method(self):
-        """
-        Test that an invalid selection method raises a ValueError.
-        """
-        fs = FeatureSelector(
-            enhanced_omics_data=self.enhanced_omics_data,
-            phenotype_data=self.phenotype_data,
-            num_features=5,
-            selection_method="invalid_method",
-        )
-        with self.assertRaises(ValueError):
-            fs.perform_feature_selection()
-
-    def test_run_feature_selection(self):
-        """
-        Test the full feature selection run.
-        """
-        fs = FeatureSelector(
-            enhanced_omics_data=self.enhanced_omics_data,
-            phenotype_data=self.phenotype_data,
-            num_features=5,
-            selection_method="correlation",
-        )
-        selected = fs.run_feature_selection()
-        self.assertEqual(selected.shape[1], 5)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_hierarchical.py b/tests/test_hierarchical.py
deleted file mode 100644
index 7be9e70..0000000
--- a/tests/test_hierarchical.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import unittest
-import pandas as pd
-from bioneuralnet.external_tools import HierarchicalClustering
-
-
-class TestHierarchicalClustering(unittest.TestCase):
-
-    def setUp(self):
-        self.adjacency_matrix = pd.DataFrame(
-            {
-                "GeneA": [1.0, 0.5, 0.2],
-                "GeneB": [0.3, 1.0, 0.7],
-                "GeneC": [0.4, 0.6, 1.0],
-            },
-            index=["GeneA", "GeneB", "GeneC"],
-        )
-
-    def test_clustering_output(self):
-        hc = HierarchicalClustering(
-            adjacency_matrix=self.adjacency_matrix,
-            n_clusters=2,
-            linkage="ward",
-            affinity="euclidean",
-            scale_data=True,
-        )
-        results = hc.run()
-        self.assertIsInstance(results["cluster_labels"], pd.DataFrame)
-        self.assertTrue(
-            isinstance(results["silhouette_score"], float)
-            or results["silhouette_score"] is None
-        )
-        unique_clusters = results["cluster_labels"]["cluster"].nunique()
-        self.assertEqual(unique_clusters, 2)
-
-    def test_without_running_clustering(self):
-        hc = HierarchicalClustering(adjacency_matrix=self.adjacency_matrix)
-        with self.assertRaises(ValueError):
-            hc.get_results()
-
-    def test_invalid_metric_with_ward_linkage(self):
-        with self.assertRaises(
-            ValueError,
-            msg="The 'ward' linkage method only supports the 'euclidean' metric.",
-        ):
-            HierarchicalClustering(
-                adjacency_matrix=self.adjacency_matrix,
-                linkage="ward",
-                affinity="cosine",
-            ).run()
-
-    def test_invalid_metric_without_ward_linkage(self):
-        with self.assertRaises(
-            ValueError, msg="Unsupported affinity metric: invalid_metric"
-        ):
-            HierarchicalClustering(
-                adjacency_matrix=self.adjacency_matrix,
-                linkage="complete",
-                affinity="invalid_metric",
-            ).run()
-
-    def test_scaling_data(self):
-        hc = HierarchicalClustering(
-            adjacency_matrix=self.adjacency_matrix,
-            n_clusters=2,
-            linkage="complete",
-            affinity="euclidean",
-            scale_data=True,
-        )
-        hc.run()
-        self.assertIsNotNone(hc.scaled_feature_matrix)
-
-    def test_no_scaling_data(self):
-        hc = HierarchicalClustering(
-            adjacency_matrix=self.adjacency_matrix,
-            n_clusters=2,
-            linkage="complete",
-            affinity="euclidean",
-            scale_data=False,
-        )
-        hc.run()
-        self.assertIsNotNone(hc.scaled_feature_matrix)
-        pd.testing.assert_frame_equal(hc.scaled_feature_matrix, self.adjacency_matrix)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_static_visualization.py b/tests/test_static_visualization.py
deleted file mode 100644
index 16e2ff2..0000000
--- a/tests/test_static_visualization.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# import pytest
-# import pandas as pd
-# import networkx as nx
-
-# from bioneuralnet.external_tools import StaticVisualizer
-# from unittest import mock
-
-
-# @pytest.fixture
-# def sample_adjacency_matrix(tmp_path):
-#     """
-#     Creates a sample adjacency matrix for testing.
-#     """
-#     data = {"gene1": [0, 1, 0], "gene2": [1, 0, 1], "gene3": [0, 1, 0]}
-#     adjacency_matrix = pd.DataFrame(data, index=["gene1", "gene2", "gene3"])
-#     return adjacency_matrix
-
-
-# @pytest.fixture
-# def mock_logger():
-#     with mock.patch("bioneuralnet.utils.logger.get_logger") as mock_get_logger:
-#         mock_logger = mock.Mock()
-#         mock_get_logger.return_value = mock_logger
-#         yield mock_logger
-
-
-# @pytest.fixture
-# def temp_output_dir(tmp_path):
-#     """
-#     Provides a temporary directory for output files.
-#     """
-#     return tmp_path / "output"
-
-
-# def test_generate_graph(sample_adjacency_matrix):
-#     """
-#     Test that StaticVisualizer generates the correct NetworkX graph from the adjacency matrix.
-#     """
-#     visualizer = StaticVisualizer(adjacency_matrix=sample_adjacency_matrix)
-#     G = visualizer.generate_graph()
-
-#     assert isinstance(G, nx.Graph)
-#     assert G.number_of_nodes() == 3
-#     assert G.number_of_edges() == 2
-#     assert "gene1" in G
-#     assert "gene2" in G
-#     assert "gene3" in G
-#     assert G.has_edge("gene1", "gene2")
-#     assert G.has_edge("gene2", "gene3")
-#     assert not G.has_edge("gene1", "gene3")
-
-
-# def test_visualize_creates_file(sample_adjacency_matrix, temp_output_dir):
-#     """
-#     Test that the visualize method creates the expected image file.
-#     """
-#     visualizer = StaticVisualizer(
-#         adjacency_matrix=sample_adjacency_matrix,
-#         output_dir=temp_output_dir,
-#         output_filename="test_static_network.png",
-#     )
-
-#     with mock.patch("bioneuralnet.utils.logger.get_logger") as mock_get_logger:
-#         mock_logger = mock.Mock()
-#         mock_get_logger.return_value = mock_logger
-#         G = visualizer.generate_graph()
-#         visualizer.visualize(G)
-
-#     output_file = temp_output_dir / "test_static_network.png"
-#     assert output_file.exists()
-#     assert output_file.is_file()
-
-
-# def test_visualize_with_empty_adjacency_matrix(tmp_path):
-#     """
-#     Test that the visualize method handles an empty adjacency matrix.
-#     """
-#     empty_adjacency = pd.DataFrame()
-#     visualizer = StaticVisualizer(
-#         adjacency_matrix=empty_adjacency,
-#         output_dir=tmp_path,
-#         output_filename="static_network_empty.png",
-#     )
-
-#     with mock.patch("bioneuralnet.utils.logger.get_logger") as mock_get_logger:
-#         mock_logger = mock.Mock()
-#         mock_get_logger.return_value = mock_logger
-#         with pytest.raises(nx.NetworkXError):
-#             G = visualizer.generate_graph()
-#             visualizer.visualize(G)
-
-
-# def test_visualize_output_directory_creation(sample_adjacency_matrix, tmp_path):
-#     """
-#     Test that the visualize method creates the output directory if it does not exist.
-#     """
-#     non_existent_dir = tmp_path / "non_existent_output"
-
-#     with mock.patch("bioneuralnet.utils.logger.get_logger") as mock_get_logger:
-#         mock_logger = mock.Mock()
-#         mock_get_logger.return_value = mock_logger
-
-#         visualizer = StaticVisualizer(
-#             adjacency_matrix=sample_adjacency_matrix,
-#             output_dir=non_existent_dir,
-#             output_filename="static_network_new_dir.png",
-#         )
-
-#         G = visualizer.generate_graph()
-#         visualizer.visualize(G)
-
-#     output_file = non_existent_dir / "static_network_new_dir.png"
-#     assert output_file.exists()
-#     assert output_file.is_file()
-
-
-# def test_visualize_with_custom_visualization_params(
-#     sample_adjacency_matrix, temp_output_dir
-# ):
-#     """
-#     Test that custom visualization parameters are applied correctly.
-#     """
-#     visualizer = StaticVisualizer(
-#         adjacency_matrix=sample_adjacency_matrix,
-#         layout="circular",
-#         node_size=500,
-#         node_color="red",
-#         edge_color="blue",
-#         linewidths=2.0,
-#         font_size=12,
-#         output_dir=temp_output_dir,
-#         output_filename="static_network_custom.png",
-#     )
-
-#     with mock.patch("bioneuralnet.utils.logger.get_logger") as mock_get_logger:
-#         mock_logger = mock.Mock()
-#         mock_get_logger.return_value = mock_logger
-#         G = visualizer.generate_graph()
-#         visualizer.visualize(G)
-
-#     output_file = temp_output_dir / "static_network_custom.png"
-#     assert output_file.exists()
-#     assert output_file.is_file()