Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study.

Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study.

The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 wome...

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Bibliographic Details
Main Authors: Sangkyu Lee, Xiaolin Liang, Meghan Woods, Anne S Reiner, Patrick Concannon, Leslie Bernstein, Charles F Lynch, John D Boice, Joseph O Deasy, Jonine L Bernstein, Jung Hun Oh
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0226157

Internet

https://doi.org/10.1371/journal.pone.0226157

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