{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:37:30Z","timestamp":1782848250146,"version":"3.54.5"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"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":["61702444"],"award-info":[{"award-number":["61702444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013494","name":"West Light Foundation of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2018-XBQNXZ-B-008"],"award-info":[{"award-number":["2018-XBQNXZ-B-008"]}],"id":[{"id":"10.13039\/501100013494","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"Chinese Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2019M653804"],"award-info":[{"award-number":["2019M653804"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Tianshan youth - Excellent Youth","award":["2019Q029"],"award-info":[{"award-number":["2019Q029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biological data to identify the association between circRNA and disease can effectively reduce cost and save time. Considering the limitations of existing computational models, we propose a semi-supervised generative adversarial network (GAN) model SGANRDA for predicting circRNA\u2013disease association. This model first fused the natural language features of the circRNA sequence and the features of disease semantics, circRNA and disease Gaussian interaction profile kernel, and then used all circRNA\u2013disease pairs to pre-train the GAN network, and fine-tune the network parameters through labeled samples. Finally, the extreme learning machine classifier is employed to obtain the prediction result. Compared with the previous supervision model, SGANRDA innovatively introduced circRNA sequences and utilized all the information of circRNA\u2013disease pairs during the pre-training process. This step can increase the information content of the feature to some extent and reduce the impact of too few known associations on the model performance. SGANRDA obtained AUC scores of 0.9411 and 0.9223 in leave-one-out cross-validation and 5-fold cross-validation, respectively. Prediction results on the benchmark dataset show that SGANRDA outperforms other existing models. In addition, 25 of the top 30 circRNA\u2013disease pairs with the highest scores of SGANRDA in case studies were verified by recent literature. These experimental results demonstrate that SGANRDA is a useful model to predict the circRNA\u2013disease association and can provide reliable candidates for biological experiments.<\/jats:p>","DOI":"10.1093\/bib\/bbab028","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T20:14:47Z","timestamp":1615925687000},"source":"Crossref","is-referenced-by-count":48,"title":["SGANRDA: semi-supervised generative adversarial networks for predicting circRNA\u2013disease associations"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-307X","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1266-2696","authenticated-orcid":false,"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao-Yuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-An","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computing, 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