LEAP: Using machine learning to support variant classification in a clinical setting.

ABSTRACT: Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogenicity (LEAP), a machine learning model that utilizes a variety of feature categories to classify variants, and achieves high performance in multiple genes and different health conditions. Feature categories include functional predictions, splice predictions, population frequencies, conservation scores, protein domain data, and clinical observation data such as personal and family history and covariant information. L2-regularized logistic regression and random forest classification models were trained on missense variants detected and classified

SUBMITTER: Lai C 

PROVIDER: S-EPMC7317941 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature