Cross-protein transfer learning substantially improves disease variant prediction.

ABSTRACT:

Background

Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity.

Results

We train cross-protein transfer (CPT) models using deep mutational scanning (DMS) data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. We also improve predictive accuracy on DMS data from held-out proteins. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this r

SUBMITTER: Jagota M 

PROVIDER: S-EPMC10408151 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature