Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML
release_unvown5h6bglbdjg2hpx5pfvga
by
Steven Hart, Eric C Polley, Hermela Shimelis, Siddhartha Yadav, Fergus J Couch
2019
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.
In application/xml+jats
format
Archived Files and Locations
application/pdf 767.1 kB
file_izocs4rl2vgmlprixyfqxx4rf4
|
www.biorxiv.org (repository) web.archive.org (webarchive) |
application/pdf 767.1 kB
file_dr2csflsgrhmvjtiortlx6aydq
|
www.biorxiv.org (repository) web.archive.org (webarchive) |
post
Stage
unknown
Date 2019-10-14
10.1101/792754
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar