{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:01Z","timestamp":1755219841639,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Interpreting and subtyping type 2 diabetes (T2D) is challenging yet essential for achieving fine-grained pathophysiological insights and precise clinical stratification. Previous studies have primarily relied on a small number of pre-selected risk factors and biomarkers, neglecting the integration of multimodality data (e.g., phenotypic and genetic features) for more comprehensive analyses. In this study, we select a cohort of 42,256 participants from the National Institutes of Health\u2019s All of Us Research Program, where our hypergraph framework achieves an AUROC of 89.64% on predicting T2D when integrating phenotypic and genetic features. The proposed pipeline performs subtyping by clustering clinical concepts, genetic variants, and individuals in an end-to-end manner. Further analysis using genetic risk scores reveals distinct genetic profiles between T2D subtypes and highlights the potential applications of our solution in precision medicine.<\/jats:p>","DOI":"10.3233\/shti251002","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:48Z","timestamp":1754566668000},"source":"Crossref","is-referenced-by-count":0,"title":["Type 2 Diabetes Subtyping via Phenotype and Genotype Co-Learning"],"prefix":"10.3233","author":[{"given":"Ziyang","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Emory University, USA"}]},{"given":"Lily","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Emory University, USA"}]},{"given":"Weimin","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Florida, USA"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Rollins School of Public Health, Emory University, USA"}]},{"given":"Hui","family":"Shao","sequence":"additional","affiliation":[{"name":"Rollins School of Public Health, Emory University, USA"}]},{"given":"Yan V.","family":"Sun","sequence":"additional","affiliation":[{"name":"Rollins School of Public Health, Emory University, USA"}]},{"given":"Jingchuan","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Florida, USA"}]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"Biostatistics and Health Data Science, School of Medicine, Indiana University, USA"}]},{"given":"Rui","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Florida, USA"}]},{"given":"Carl","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Emory University, USA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:49Z","timestamp":1754566669000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251002","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}