Disease-specific variant pathogenicity prediction using multimodal biomedical language models.

ABSTRACT: Missense variants play a key role in the diagnosis of genetic disorders and in disease risk prediction. Existing methods focus primarily on the prediction of variant effects in terms of their deleteriousness, without taking into account the disease-specific context, and are therefore limited in terms of their utility in real-world diagnosis and decision making. Here, we introduce disease-specific variant pathogenicity prediction (DIVA), a novel deep learning framework that directly predicts specific disease types alongside the probability of deleteriousness for missense variants. Our approach integrates information from two different modalities - protein sequence and disease-related textual annotations - encoded using two pre-trained language models and optimized within a contrastive learn

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12458274 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature