Graphical Dirichlet process.
A new Gaussian graphical model that produces subject-specific and predictive graphs with theoretical guarantee.
Individualized causal discovery.
Graphical Dirichlet process.
A new Gaussian graphical model that produces subject-specific and predictive graphs with theoretical guarantee.
The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell Receptor (TCR) repertoire sequencing is a relatively new technology that …
A review of Bayesian graphical models for biological applications.
A review of Bayesian graphical models for biological applications.
Carcinoma–associated fibroblasts (CAF) are a heterogeneous group of cells within the tumor microenvironment (TME) that can promote tumorigenesis in the prostate. By understanding the mechanism(s) by which CAF contributes to tumor growth, new …
A Bayesian hierarchical varying-sparsity regression (BEHAVIOR) model that selects clinically relevant disease markers by integrating proteogenomic and clinical data.
A new directed acyclic graphical model that produces subject-specific and predictive graphs with theoretical guarantee.
We develop a parallel-tempered feature allocation algorithm to infer tumor heterogeneity from deep DNA sequencing data. The feature allocation model is based on a binomial likelihood and an Indian Buffet process prior on the latent haplotypes. A …
A hierarchical reciprocal graphical models to infer gene networks from heterogeneous data with or without known groups.
A Gaussian reciprocal graphical model for inference about gene regulatory relationships by integrating mRNA gene expression and DNA level information including copy number and methylation.
A review of directed, undirected, and reciprocal graphs.
An array-variate directed acyclic graphical model for tensor data.
A dose insertion design for phase I/II clinical trials in oncology based on both efficacy and toxicity outcomes.
Bayesian methods have found many successful applications in high-throughput genomics. We focus on approaches for network-based inference from gene expression data. Methods that employ sparse priors have been particularly successful, as they are …
A Bayesian directed acyclic graphical model to recover the structure of nonlinear gene regulatory networks.
An integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient’s clinical outcome.