A machine learning approach for somatic mutation discovery-Reference-Cited by-同舟云学术

A machine learning approach for somatic mutation discovery

Published:2018-09-05 Issue:457 Volume:10 Page:
ISSN:1946-6234
Container-title:Science Translational Medicine
language:en
Short-container-title:Sci. Transl. Med.

Author:

Wood Derrick E.¹^ORCID,White James R.¹,Georgiadis Andrew¹,Van Emburgh Beth¹,Parpart-Li Sonya¹,Mitchell Jason¹,Anagnostou Valsamo²^ORCID,Niknafs Noushin²^ORCID,Karchin Rachel²³^ORCID,Papp Eniko¹^ORCID,McCord Christine¹,LoVerso Peter¹,Riley David¹,Diaz Luis A.⁴^ORCID,Jones Siân¹,Sausen Mark¹,Velculescu Victor E.²^ORCID,Angiuoli Samuel V.¹^ORCID

Affiliation:

1. Personal Genome Diagnostics, Baltimore, MD 21224, USA.

2. The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

3. Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA.

4. Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

Abstract

A machine learning approach to detect somatic mutations in cancer patients outperforms existing methods and may improve clinical outcome.

Funder

National Institutes of Health

LUNGevity Foundation

Commonwealth Foundation

Dr. Miriam and Sheldon G. Adelson Medical Research Foundation