{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:12:42Z","timestamp":1775142762947,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T00:00:00Z","timestamp":1656806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0712403"],"award-info":[{"award-number":["2020YFA0712403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932008"],"award-info":[{"award-number":["61932008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["B18015"],"award-info":[{"award-number":["B18015"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14\u00a0010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.<\/jats:p>","DOI":"10.1093\/bib\/bbac255","type":"journal-article","created":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T10:26:28Z","timestamp":1656843988000},"source":"Crossref","is-referenced-by-count":17,"title":["MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7766-6498","authenticated-orcid":false,"given":"Guiying","family":"Dong","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain-inspired Intelligence, Fudan University , Shanghai, 200433, China"},{"name":"MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University , Shanghai, 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5747-3093","authenticated-orcid":false,"given":"Zi-Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-inspired Intelligence, Fudan University , Shanghai, 200433, China"},{"name":"MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University , Shanghai, 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-inspired Intelligence, Fudan University , Shanghai, 200433, China"},{"name":"MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University , Shanghai, 200433, China"},{"name":"Zhangjiang Fudan International Innovation Center , Shanghai, 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing-Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-inspired Intelligence, Fudan University , Shanghai, 200433, China"},{"name":"MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University , Shanghai, 200433, China"},{"name":"Zhangjiang Fudan International Innovation Center , Shanghai, 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,7,3]]},"reference":[{"key":"2022071906191323300_ref1","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1186\/s13073-021-00927-6","article-title":"A global overview of genetically interpretable multimorbidities among 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