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

Steven N. Hart, Eric C. Polley, Hermella Shimelis, Siddhartha Yadav, Fergus J. Couch

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number13
Journalnpj Breast Cancer
Volume6
Issue number1
DOIs
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Pharmacology (medical)

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