Phenotypic prediction of missense variants via deep contrastive learning.
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| Title: | Phenotypic prediction of missense variants via deep contrastive learning. |
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| Authors: | Wen J; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; VA Boston Healthcare System, Boston, MA, USA.; Biological and Life Sciences Division, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates., Zeng S; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA., Bonzel CL; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; VA Boston Healthcare System, Boston, MA, USA., Kobren SN; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Du J; Department of Statistics, University of Chicago, Chicago, IL, USA., Chai Y; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Wang H; Department of Computer Science, Rutgers University, Piscataway, NJ, USA., Zhu M; Department of Genetics, Harvard Medical School, Boston, MA, USA., Chen S; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Department of Human Genetics, The University of Chicago, Chicago, IL, USA., Leng F; Howard Hughes Medical Institute and Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA., Zhang HG; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA., Liao KP; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; VA Boston Healthcare System, Boston, MA, USA.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Boston, MA, USA., Cho K; VA Boston Healthcare System, Boston, MA, USA.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Boston, MA, USA., Kohane IS; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Zitnik M; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Boston, MA, USA.; Harvard Data Science Initiative, Cambridge, MA, USA., Pereira AC; Brigham and Women's Hospital, Boston, MA, USA., Liu JS; Department of Statistics and Data Science, Tsinghua University, Beijing, China.; Department of Statistics, Harvard University, Cambridge, MA, USA., Cai T; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. tcai@hsph.harvard.edu.; VA Boston Healthcare System, Boston, MA, USA. tcai@hsph.harvard.edu.; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. tcai@hsph.harvard.edu. |
| Source: | Nature biomedical engineering [Nat Biomed Eng] 2026 Apr 14. Date of Electronic Publication: 2026 Apr 14. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer Nature Country of Publication: England NLM ID: 101696896 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2157-846X (Electronic) Linking ISSN: 2157846X NLM ISO Abbreviation: Nat Biomed Eng Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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