Year: 2020
Type: article
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.
Source: npj Breast Cancer
Institution: Mayo Clinic
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
Awards: G1357738703, G2202675246, G2731186574, G2744195702, G2983955638, BRCA2 missense mutations and breast cancer, G424082930, G431899019, Risk and penetrance of mutations from breast cancer testing panels., G4893741037, G6552302344, G7030639063, G7088314342, BRCA1 and BRCA2 missense mutations and breast cancer risk, Identifying and validating novel susceptibility genes for breast cancer, G9000101281, Mayo Clinic Breast Cancer SPORE
OpenAlex ID: W3022465046