{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:21:40Z","timestamp":1768267300594,"version":"3.49.0"},"reference-count":76,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:00:00Z","timestamp":1699228800000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1201200"],"award-info":[{"award-number":["2021YFF1201200"]}],"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":["62350004"],"award-info":[{"award-number":["62350004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1909208"],"award-info":[{"award-number":["U1909208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972423"],"award-info":[{"award-number":["61972423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Major Project of Changsha","award":["kh2202004"],"award-info":[{"award-number":["kh2202004"]}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2022B03023"],"award-info":[{"award-number":["2022B03023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene\u2013phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address the problem of labeled-data scarcity, we propose a self-supervised learning strategy for gene\u2013phenotype association prediction, called SSLpheno. Our approach utilizes an attributed network that integrates protein\u2013protein interactions and gene ontology data. We apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation. Specifically, we calculate the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. We employ a deep neural network for multi-label classification of phenotypes in the downstream task. Our experimental results demonstrate that SSLpheno outperforms state-of-the-art methods, especially in categories with fewer annotations. Moreover, our case studies illustrate the potential of SSLpheno as an effective prescreening tool for gene\u2013phenotype association identification.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/bixuehua\/SSLpheno.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad662","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T11:45:26Z","timestamp":1699530326000},"source":"Crossref","is-referenced-by-count":7,"title":["SSLpheno: a self-supervised learning approach for gene\u2013phenotype association prediction using protein\u2013protein interactions and gene ontology data"],"prefix":"10.1093","volume":"39","author":[{"given":"Xuehua","family":"Bi","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"},{"name":"Medical Engineering and Technology College, Xinjiang Medical University , Urumqi 830017, China"}]},{"given":"Weiyang","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University , Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8319-9793","authenticated-orcid":false,"given":"Qichang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]}],"member":"286","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"2023112311351429900_btad662-B1","doi-asserted-by":"crossref","first-page":"dmm049441","DOI":"10.1242\/dmm.049441","article-title":"Contribution of model organism phenotypes to the computational identification of human disease genes","volume":"15","author":"Alghamdi","year":"2022","journal-title":"Dis Model Mech"},{"key":"2023112311351429900_btad662-B2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1001\/jama.2021.20356","article-title":"Phenome-wide association studies","volume":"327","author":"Bastarache","year":"2022","journal-title":"JAMA"},{"key":"2023112311351429900_btad662-B3","first-page":"D933","article-title":"GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies","volume":"48","author":"Beck","year":"2020","journal-title":"Nucleic Acids 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