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September 16, 2025Open Access

Disease-specific variant pathogenicity prediction using multimodal biomedical language models

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A new deep learning method predicts variant effects in disease-specific contexts, suggesting improved clinical interpretation.

DIVA accurately predicts disease-specific pathogenicity of missense variants, enhancing clinical utility for diagnosis.
The integration of protein sequences and disease annotations within a contrastive learning framework significantly improves predictions.
Benchmark results show that DIVA outperforms existing baseline methods in classifying variant deleteriousness.
Utilizing AlphaMissense scores within DIVA boosts the accuracy of deleterious variant classification significantly.

Liu et al. (2025) studied this question.

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September 16, 2025Open Access

View Full Paper

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Authors

A new deep learning method predicts variant effects in disease-specific contexts, suggesting improved clinical interpretation.

DIVA accurately predicts disease-specific pathogenicity of missense variants, enhancing clinical utility for diagnosis.
The integration of protein sequences and disease annotations within a contrastive learning framework significantly improves predictions.
Benchmark results show that DIVA outperforms existing baseline methods in classifying variant deleteriousness.
Utilizing AlphaMissense scores within DIVA boosts the accuracy of deleterious variant classification significantly.

Liu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark