DS-MVP: identifying disease-specific pathogenicity of missense variants by pre-training representation.

ABSTRACT: Accurately predicting the pathogenicity of missense variants is crucial for improving disease diagnosis and advancing clinical research. However, existing computational methods primarily focus on general pathogenicity predictions, overlooking assessments of disease-specific conditions. In this study, we propose DS-MVP, a method capable of predicting disease-specific pathogenicity of missense variants in human genomes. DS-MVP first leverages a deep learning model pre-trained on a large general pathogenicity dataset to learn rich representation of missense variants. It then fine-tunes these representations with an XGBoost model on smaller datasets for specific diseases. We evaluated the learned representation by testing it on multiple binary pathogenicity datasets and gene-level statistics,

SUBMITTER: Chen Q 

PROVIDER: S-EPMC11932084 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature