Functional causal discovery.
Individualized causal discovery.
Zero-inflated generalized hypergeometric Bayesian networks.
Bayesian federated learning.
Graphical Dirichlet process.
Discover causality from observation categorical data.
Discover causality from heterogeneous continuous observational data with directed cyclic graphs.
Discover causality from observation ordinal categorical data with ordinal Bayesian networks.
A review of Bayesian graphical models for biological applications.
A review of Bayesian graphical models for biological applications.
Zero-inflated Poisson Bayesian networks.
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.
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.
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.