We are a group of biostatisticians and data scientists, working at the intersection of statistics, biology and medicine. Our research explores the potential of Bayesian probabilistic models and machine learning methods to assist in medical and health sciences. These methods are motivated by large and complex datasets such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. A special focus is on developing integrative models combining different sources of data for biomarker discovery and clinical prediction to aid precision/translational medicine.
BayesRx Group
- 14 followers
- United States of America
- https://bayesrx.github.io/
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TransPRECISE
TransPRECISE PublicPersonalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome
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- .github Public
bayesrx/.github’s past year of commit activity - mSGR Public
bayesrx/mSGR’s past year of commit activity - GraphR Public
GraphR: a probabilistic modeling framework for genomic networks incorporating sample heterogeneity.
bayesrx/GraphR’s past year of commit activity - SLAC Public
bayesrx/SLAC’s past year of commit activity - rBGR Public
About rBGR is an R package that implements a flexible Bayesian model to construct heterogeneous graphs under non-normal continuous data. For more details, please see Yao et al. (2023+) Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks.
bayesrx/rBGR’s past year of commit activity - DreameSpase Public
bayesrx/DreameSpase’s past year of commit activity - ultrametricMat Public
The ultrametricMat is a package designed to conduct the Bayesian inference on the ultrametric matrices by the leveraging the bijection map between the ultrametric matrices and the tree space. We refer to more details to Yao et al. (2023+) Geometry-driven Bayesian Inference for Ultrametric Covariance Matrices.
bayesrx/ultrametricMat’s past year of commit activity - SPARTIN Public
bayesrx/SPARTIN’s past year of commit activity
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