AI for Scientific Discovery in Omics Data-Driven Precision Medicine.

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Bibliographic Details
Title: AI for Scientific Discovery in Omics Data-Driven Precision Medicine.
Authors: Li F; Associate Director of the Center for Translational Bioinformatics and an Associate Professor in the Institute for Informatics, Data Science and Biostatistics, the Department of Pediatrics, Washington University School of Medicine, and the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA., Zhang H; PhD student in the Roy and Diana Vagelos Division of Biology & Biomedical Sciences program at Washington University, St. Louis, Missouri, USA., Huang D; PhD student in the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA., Li H; Bioinformatics research analyst in the Institute for Informatics, Data Science and Biostatistics at Washington University, St. Louis, Missouri, USA., Li W; PhD student in the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA., Xu T; Bioinformatics research analyst in the Institute for Informatics, Data Science and Biostatistics at Washington University, St. Louis, Missouri, USA., Chen Y; Professor in the Department of Computer Science and Engineering at Washington University, St. Louis, Missouri, USA., Province M; Professor in the Department of Genetics, Washington University School of Medicine at Washington University, St. Louis, Missouri, USA., Payne PRO; Director, Institute for Informatics, Data Science, and Biostatistics, the Janet and Bernard Becker Professor, Vice Chancellor for Biomedical Informatics and Data Science, WashU Medicine, and Chief Health AI Officer at BJC Health System and Washington University Medicine, St. Louis, Missouri, USA.
Source: Missouri medicine [Mo Med] 2026 Jan-Feb; Vol. 123 (1), pp. 55-66.
Publication Type: Journal Article
Journal Info: Publisher: Missouri State Medical Association Country of Publication: United States NLM ID: 0400744 Publication Model: Print Cited Medium: Internet ISSN: 0026-6620 (Print) Linking ISSN: 00266620 NLM ISO Abbreviation: Mo Med Subsets: MEDLINE; In Process
Database: MEDLINE Ultimate
Description
ISSN:0026-6620