Cross-protein transfer learning substantially improves disease variant prediction.

Abstract excerpt

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)...

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