NetDiff - Bayesian model selection for differential gene regulatory network inference.

ABSTRACT: Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more comput

SUBMITTER: Thorne T 

PROVIDER: S-EPMC5159802 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature