{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:16:20Z","timestamp":1776888980235,"version":"3.51.2"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872094"],"award-info":[{"award-number":["61872094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"DOI":"10.13039\/100020441","name":"Shanghai Center for Brain Science and Brain-Inspired Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100020441","id-type":"DOI","asserted-by":"crossref"}]},{"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"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2017SHZDZX01"],"award-info":[{"award-number":["2017SHZDZX01"]}]},{"name":"Information Technology Facility"},{"name":"CAS-MPG Partner Institute for Computational Biology"},{"name":"Shanghai Institute for Biological Sciences"},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["315896"],"award-info":[{"award-number":["315896"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"name":"JST ACCEL","award":["JPMJAC1503"],"award-info":[{"award-number":["JPMJAC1503"]}]},{"name":"MEXT KAKENHI","award":["19H04169"],"award-info":[{"award-number":["19H04169"]}]},{"name":"MEXT KAKENHI","award":["21H05027"],"award-info":[{"award-number":["21H05027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Deciphering the relationship between human genes\/proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human disorders. However, the current HPO annotations are still incomplete. Thus, it is necessary to computationally predict human protein\u2013phenotype associations. In terms of current, cutting-edge computational methods for annotating proteins (such as functional annotation), three important features are (i) multiple network input, (ii) semi-supervised learning and (iii) deep graph convolutional network (GCN), whereas there are no methods with all these features for predicting HPO annotations of human protein.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We develop HPODNets with all above three features for predicting human protein\u2013phenotype associations. HPODNets adopts a deep GCN with eight layers which allows to capture high-order topological information from multiple interaction networks. Empirical results with both cross-validation and temporal validation demonstrate that HPODNets outperforms seven competing state-of-the-art methods for protein function prediction. HPODNets with the architecture of deep GCNs is confirmed to be effective for predicting HPO annotations of human protein and, more generally, node label ranking problem with multiple biomolecular networks input in bioinformatics.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/liulizhi1996\/HPODNets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab729","type":"journal-article","created":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T23:41:40Z","timestamp":1634600500000},"page":"799-808","source":"Crossref","is-referenced-by-count":13,"title":["HPODNets: deep graph convolutional networks for predicting human protein\u2013phenotype associations"],"prefix":"10.1093","volume":"38","author":[{"given":"Lizhi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University , Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Mamitsuka","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Kyoto Prefecture 611-0011, Japan"},{"name":"Department of Computer Science, Aalto University , Espoo 02150, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6067-5312","authenticated-orcid":false,"given":"Shanfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai 200433, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China"},{"name":"MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433, China"},{"name":"Zhangjiang Fudan International Innovation Center , Shanghai 200433, China"},{"name":"Shanghai Key Lab of Intelligent Information Processing, Fudan University , Shanghai 200433, China"},{"name":"Institute of Artificial Intelligence Biomedicine, Nanjing University , Nanjing 210032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"2023020108502165600_btab729-B1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J. 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