{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:18:35Z","timestamp":1784891915650,"version":"3.55.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"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\/501100008855","name":"Zaozhuang University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008855","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major special projects of the Ministry of Science and Technology","award":["2021ZD0200403"],"award-info":[{"award-number":["2021ZD0200403"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62120106008"],"award-info":[{"award-number":["62120106008"]}],"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":["62172355"],"award-info":[{"award-number":["62172355"]}],"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":["62076085"],"award-info":[{"award-number":["62076085"]}],"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":["61976077"],"award-info":[{"award-number":["61976077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA\u2013disease associations, and uncovering new circRNA\u2013disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases. Firstly, we computed different biological attribute information of circRNA and disease separately and used them to construct multiple multi-source similarity networks. Then, we used the high-order GCN algorithm to learn feature embedding representations with high-order mixed neighborhood information of circRNA and disease from the constructed multi-source similarity networks, respectively. Finally, the deep neural network classifier was implemented to predict associations of circRNAs with diseases. The MNMDCDA model obtained AUC scores of 95.16%, 94.53%, 89.80% and 91.83% on four benchmark datasets, i.e., CircR2Disease, CircAtlas v2.0, Circ2Disease and CircRNADisease, respectively, using the 5-fold cross-validation approach. Furthermore, 25 of the top 30 circRNA\u2013disease pairs with the best scores of MNMDCDA in the case study were validated by recent literature. Numerous experimental results indicate that MNMDCDA can be used as an effective computational tool to predict circRNA\u2013disease associations and can provide the most promising candidates for biological experiments.<\/jats:p>","DOI":"10.1093\/bib\/bbac479","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:41:40Z","timestamp":1668645700000},"source":"Crossref","is-referenced-by-count":20,"title":["MNMDCDA: prediction of circRNA\u2013disease associations by learning mixed neighborhood information from multiple distances"],"prefix":"10.1093","volume":"23","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology , Hefei 230601 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue-Gang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology , Hefei 230601 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-307X","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences , Nanning 530007 , China"},{"name":"College of Information Science and Engineering, Zaozhuang University , Shandong 277100 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei-Pei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology , Hefei 230601 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[{"name":"Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences , Nanning 530007 , China"},{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an Shaanxi 710129 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"issue":"5","key":"2022112111203787900_ref1","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/onc.2017.361","article-title":"Circular RNAs in cancer: opportunities and challenges in the field","volume":"37","author":"Kristensen","year":"2018","journal-title":"Oncogene"},{"key":"2022112111203787900_ref2","doi-asserted-by":"crossref","first-page":"5075","DOI":"10.1109\/JBHI.2022.3199462","article-title":"NSECDA: natural semantic enhancement for circRNA-disease association prediction","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7441","key":"2022112111203787900_ref3","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nature11928","article-title":"Circular RNAs are a large class of animal RNAs with 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