PhenoNet: identification of key networks associated with disease phenotype | Zendy

Rotem BenHamo | Zendy; Moriah Gidoni | Zendy; Sol Efroni | Zendy

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PhenoNet: identification of key networks associated with disease phenotype

Author(s) -

Rotem BenHamo,

Moriah Gidoni,

Sol Efroni

Publication year - 2014

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btu199

Subject(s) - identification (biology) , phenotype , computational biology , transcriptome , key (lock) , clinical phenotype , biology , computer science , systems biology , bioinformatics , gene , genetics , gene expression , ecology , botany

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.

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