PhenoNet: identification of key networks associated with disease phenotype

Rotem Ben-Hamo, Moriah Gidoni, Sol Efroni
2014 Bioinformatics  
Motivation: At the core of transcriptome analyses of cancer is a challenge to detect molecular differences affiliated with disease phenotypes. This approach has led to remarkable progress in identifying molecular signatures and in stratifying patients into clinical groups. Yet, despite this progress, many of the identified signatures are not robust enough to be clinically used and not consistent enough to provide a follow-up on molecular mechanisms. Results: To address these issues, we
more » ... PhenoNet, a novel algorithm for the identification of pathways and networks associated with different phenotypes. PhenoNet uses two types of input data: gene expression data (RMA, RPKM, FPKM, etc.) and phenotypic information, and integrates these data with curated pathways and protein-protein interaction information. Comprehensive iterations across all possible pathways and subnetworks result in the identification of key pathways or subnetworks that distinguish between the two phenotypes. Availability and implementation: Matlab code is available upon request.
doi:10.1093/bioinformatics/btu199 pmid:24812342 fatcat:7lzculwmfjhmvexb5auakeqa7a