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PhenoNet: identification of key networks associated with disease phenotype
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
doi:10.1093/bioinformatics/btu199
pmid:24812342
fatcat:7lzculwmfjhmvexb5auakeqa7a