Phenotypic prediction of missense variants via deep contrastive learning
Wen, Jun Zeng, Sihang Bonzel, Clara-Lea Kobren, Shilpa Nadimpalli Du, Jiangchuan Chai, Yi Wang, Hao Zhu, Meng Chen, Siwei Leng, Fangwei
Wen, Jun
Zeng, Sihang
Bonzel, Clara-Lea
Kobren, Shilpa Nadimpalli
Du, Jiangchuan
Chai, Yi
Wang, Hao
Zhu, Meng
Chen, Siwei
Leng, Fangwei
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Computational Biology
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Journal article
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English
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Abstract
Missense variants (MVs) influence clinical phenotypes, but our understanding of their phenotypic consequences remains constrained. Existing computational approaches to interpret MVs predominantly assess their pathogenicity, without considering phenotypic heterogeneity. We present a machine-learning-based method, PheMART, to predict the clinical phenotypic consequences of MVs. PheMART integrates comprehensive variant and phenotype characterizations by leveraging a robust combination of multiple resources involving protein language models, protein–protein interactions, protein domains, medical knowledge graphs and electronic health records. Exploiting contrastive learning, PheMART establishes connections between MVs and 4,179 phenotypes by jointly projecting them into a cohesive low-dimensional metric space where proximity signifies relevance. Besides substantially outperforming existing models, PheMART aids in diagnosing individuals with rare diseases by effectively pinpointing clinical diagnoses and causative MVs. As a resource to the community, we provide a database of phenotypic predictions for 5.1 million putative pathogenic amino acid alterations.
Citation
J. Wen, S. Zeng, C.-L. Bonzel, S.N. Kobren, J. Du, Y. Chai , et al., "Phenotypic prediction of missense variants via deep contrastive learning," Nature Biomedical Engineering, pp. 1-16, 2026, https://doi.org/10.1038/s41551-026-01636-4.
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Nature Biomedical Engineering
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40 Engineering, 4003 Biomedical Engineering
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Springer Nature