Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

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ID: 118027
2018
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Abstract
Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway …
Reference Key
gp2018cellmachine Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Way GP;Sanchez-Vega F;La K;Armenia J;Chatila WK;Luna A;Sander C;Cherniack AD;Mina M;Ciriello G;Schultz N; ;Sanchez Y;Greene CS;;
Journal Cell reports
Year 2018
DOI
DOI not found
URL
https://www.pubmed.gov/29617658/
Keywords
Tumor National Center for Biotechnology Information NCBI NLM MEDLINE gene expression regulation humans pubmed abstract nih national institutes of health national library of medicine research support non-u.s. gov't N.I.H. Extramural machine learning* cell line neoplasms / genetics* genome human neoplasms / metabolism signal transduction neoplastic pmid:29617658 pmc5918694 doi:10.1016/j.celrep.2018.03.046 gregory p way francisco sanchez-vega casey s greene ras proteins / genetics* ras proteins / metabolism

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