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Article

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

PloS one - 1 Jan 2020

Lee Sangkyu, Liang Xiaolin, Woods Meghan, Reiner Anne S, Concannon Patrick, Bernstein Leslie, Lynch Charles F, Boice John D, Deasy Joseph O, Bernstein Jonine L, Oh Jung Hun

Abstract excerpt

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 women with unilateral breast cancer were identified within the Women's Environmental Cancer and Radiation Epidemiology...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Source

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Identifiers

PMID: 32106268
DOI: 10.1371/journal.pone.0226157
PMCID: PMC7046218

Genes

ATM

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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