Identifying protein interaction subnetworks by a bagging Markov random field-based method.

ABSTRACT: Identification of differentially expressed subnetworks from protein-protein interaction (PPI) networks has become increasingly important to our global understanding of the molecular mechanisms that drive cancer. Several methods have been proposed for PPI subnetwork identification, but the dependency among network member genes is not explicitly considered, leaving many important hub genes largely unidentified. We present a new method, based on a bagging Markov random field (BMRF) framework, to improve subnetwork identification for mechanistic studies of breast cancer. The method follows a maximum a posteriori principle to form a novel network score that explicitly considers pairwise gene interactions in PPI networks, and it searches for subnetworks with maximal network scores. To improve th

SUBMITTER: Chen L 

PROVIDER: S-EPMC3553975 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature