A new Gaussian graphical model that produces subject-specific and predictive graphs with theoretical guarantee.
Individualized causal discovery.
Zero-inflated generalized hypergeometric Bayesian networks.
Bayesian federated learning.
Bayesian multi-way clustering.
Graphical Dirichlet process.
A new Gaussian graphical model that produces subject-specific and predictive graphs with theoretical guarantee.
Bayesian integrative matrix factorization.
A review of Bayesian graphical models for biological applications.
A review of Bayesian graphical models for biological applications.
Bayesian biclustering via multinomial matrix factorization.
Zero-inflated Poisson Bayesian networks.
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 novel polygenic prediction method that infers posterior SNP effect sizes using GWAS summary statistics and an external LD reference panel.
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.