Ben-Hamo R, Gidoni M, Efroni S. PhenoNet: identification of key networks associated with disease phenotype.
Bioinformatics 2014;
30:2399-405. [PMID:
24812342 DOI:
10.1093/bioinformatics/btu199]
[Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
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 introduce 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.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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