PhenoNet: Identification of key networks associated with disease phenotype

Rotem Ben-Hamo, Moriah Gidoni, Sol Efroni

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.

Original languageEnglish
Pages (from-to)2399-2405
Number of pages7
Issue number17
StatePublished - 1 Sep 2014

Bibliographical note

The results published here are fully or partially based on data generated by The Cancer Genome Atlas pilot project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions constituting the TCGA research network can be found at the project website (

Funding: Part of this work has been funded by the Israeli Cancer Association (grant no. 20132014).


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