Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure.

ABSTRACT:

Background

Biological insights into group differences, such as disease status, have been achieved through differential co-expression analysis of microarray data. Additional understanding of group differences may be achieved by integrating the connectivity structure of the differential co-expression network and per-gene differential expression between phenotypic groups. Such a global differential co-expression network strategy may increase sensitivity to detect gene-gene interactions (or expression epistasis) that may act as candidates for rewiring susceptibility co-expression networks.

Methods

We test two methods for inferring Genetic Association Interaction Networks (GAIN) incorporating both differential co-expression effects and differential expression effects: a generaliz

SUBMITTER: Lareau CA 

PROVIDER: S-EPMC4326454 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature