Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.

ABSTRACT: Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of genes related to a particular disease perform better than those trained using all variants, due to their higher specificity, despite the smaller training dataset size. In this study, we further investigated the advantages of "gene-specific" machine learning compared to "disease-specific" machine learning. We used 1068 rare (gnomAD minor allele frequency (MAF) < 0.005) missense variants of 28 genes associated with hereditary cancers for our investigation. Popular machine learning classifiers were employed: regularized logistic regression, extrem

SUBMITTER: Kang M 

PROVIDER: S-EPMC10307865 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature