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Article

A machine learning approach for somatic mutation discovery.

Science translational medicine - 5 Sept 2018

Wood Derrick E, White James R, Georgiadis Andrew, Van Emburgh Beth, Parpart-Li Sonya, Mitchell Jason, Anagnostou Valsamo, Niknafs Noushin, Karchin Rachel, Papp Eniko, McCord Christine, LoVerso Peter, Riley David, Diaz Luis A, Jones Siân, Sausen Mark, Velculescu Victor E, Angiuoli Samuel V

Abstract excerpt

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98%...

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PMID: 30185652
DOI: 10.1126/scitranslmed.aar7939
PMCID: PMC6481619

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