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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, demonstrating that DS-MVP outperforms existing state-of-the-art methods, such as MetaRNN and AlphaMissense. Additionally, DS-MVP excels in multi-label and multi-class classification, effectively classifying disease-specific pathogenic missense variants based on disease conditions. It further enhances predictions by fine-tuning the pre-trained model on disease-specific datasets. Finally, we analyzed the contributions of the pre-trained model and various feature types, with gene description corpus features from large language model and genetic feature fusion contributing the most. These results underscore that DS-MVP represents a broader perspective on pathogenicity prediction and holds potential as an effective tool for disease diagnosis.<\/jats:p>","DOI":"10.1093\/bib\/bbaf119","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T04:35:59Z","timestamp":1743222959000},"source":"Crossref","is-referenced-by-count":3,"title":["DS-MVP: identifying disease-specific pathogenicity of missense variants by pre-training representation"],"prefix":"10.1093","volume":"26","author":[{"given":"Qiufeng","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Jiangsu 215006 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Quan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Jiangsu 215006 ,","place":["China"]},{"name":"Collaborative Innovation Center of Novel 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