Bayesian inference of hub nodes across multiple networks.

ABSTRACT: Hub nodes within biological networks play a pivotal role in determining phenotypes and disease outcomes. In the multiple network setting, we are interested in understanding network similarities and differences across different experimental conditions or subtypes of disease. The majority of proposed approaches for joint modeling of multiple networks focus on the sharing of edges across graphs. Rather than assuming the network similarities are driven by individual edges, we instead focus on the presence of common hub nodes, which are more likely to be preserved across settings. Specifically, we formulate a Bayesian approach to the problem of multiple network inference which allows direct inference on shared and differential hub nodes. The proposed method not only allows a more intuitive inte

SUBMITTER: Kim J 

PROVIDER: S-EPMC6393214 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature