A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD.

ABSTRACT: In this paper, we propose a Bayesian hierarchical approach to infer network structures across multiple sample groups where both shared and differential edges may exist across the groups. In our approach, we link graphs through a Markov random field prior. This prior on network similarity provides a measure of pairwise relatedness that borrows strength only between related groups. We incorporate the computational efficiency of continuous shrinkage priors, improving scalability for network estimation in cases of larger dimensionality. Our model is applied to patient groups with increasing levels of chronic obstructive pulmonary disease severity, with the goal of better understanding the break down of gene pathways as the disease progresses. Our approach is able to identify critical hub genes

SUBMITTER: Shaddox E 

PROVIDER: S-EPMC8078135 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature