A review of Bayesian graphical models for biological applications.
Zero-inflated Poisson Bayesian networks.
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.
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.
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.