Predicting functional effect of missense variants using graph attention neural networks.

ABSTRACT: Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10361701 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature