Reconciling differential gene expression data with molecular interaction networks.

ABSTRACT:

Motivation

Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease.

Results

To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P-values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four metho

SUBMITTER: Poirel CL 

PROVIDER: S-EPMC3582271 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature