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Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML

Authors
  • Hart, Steven N.1
  • Polley, Eric C.1
  • Shimelis, Hermella1
  • Yadav, Siddhartha1
  • Couch, Fergus J.1, 1
  • 1 Mayo Clinic, Rochester, MN, USA , Rochester (United States)
Type
Published Article
Journal
npj Breast Cancer
Publisher
Nature Publishing Group UK
Publication Date
Apr 29, 2020
Volume
6
Issue
1
Identifiers
DOI: 10.1038/s41523-020-0159-x
Source
Springer Nature
License
Green

Abstract

In silico predictions of missense variants is an important consideration when interpreting variants of uncertain significance (VUS) in the BRCA1 and BRCA2 genes. We trained and evaluated hundreds of machine learning algorithms based on results from validated functional assays to better predict missense variants in these genes as damaging or neutral. This new optimal “BRCA-ML” model yielded a substantially more accurate method than current algorithms for interpreting the functional impact of variants in these genes, making BRCA-ML a valuable addition to data sources for VUS classification.

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