Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Daniele Ramazzotti; Avantika Lal; Bo Wang; Serafim Batzoglou; Arend Sidow

doi:10.1038/s41467-018-06921-8

Nature Communications (Oct 2018)

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Daniele Ramazzotti,
Avantika Lal,
Bo Wang,
Serafim Batzoglou,
Arend Sidow

Affiliations

Daniele Ramazzotti: Department of Pathology, Stanford University
Avantika Lal: Department of Pathology, Stanford University
Bo Wang: Department of Computer Science, Stanford University
Serafim Batzoglou: Department of Computer Science, Stanford University
Arend Sidow: Department of Pathology, Stanford University

DOI: https://doi.org/10.1038/s41467-018-06921-8
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 14

Abstract

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Identifying molecular subtypes of cancer can improve personalized treatment. Here the authors present CIMLR, an algorithm that integrates multi-omic data to reveal cancer subtypes; subtypes discovered by CIMLR differ in activity of cancer-associated pathways and are significantly predictive of patient outcomes.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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