{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T14:50:59Z","timestamp":1784731859222,"version":"3.55.0"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["22A0101"],"award-info":[{"award-number":["22A0101"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["22A0350"],"award-info":[{"award-number":["22A0350"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborious. Meanwhile, computational models can effectively predict potential circRNA\u2013disease associations (CDAs), but are restricted by limited data, resulting in data with high dimension and imbalance. In this study, we propose a model based on automatically selected meta-path and contrastive learning, called the MPCLCDA model. First, the model constructs a new heterogeneous network based on circRNA similarity, disease similarity and known association, via automatically selected meta-path and obtains the low-dimensional fusion features of nodes via graph convolutional networks. Then, contrastive learning is used to optimize the fusion features further, and obtain the node features that make the distinction between positive and negative samples more evident. Finally, circRNA\u2013disease scores are predicted through a multilayer perceptron. The proposed method is compared with advanced methods on four datasets. The average area under the receiver operating characteristic curve, area under the precision-recall curve and F1 score under 5-fold cross-validation reached 0.9752, 0.9831 and 0.9745, respectively. Simultaneously, case studies on human diseases further prove the predictive ability and application value of this method.<\/jats:p>","DOI":"10.1093\/bib\/bbad227","type":"journal-article","created":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T03:06:31Z","timestamp":1686971191000},"source":"Crossref","is-referenced-by-count":67,"title":["MPCLCDA: predicting circRNA\u2013disease associations by using automatically selected meta-path and contrastive learning"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2009-6069","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Xiangtan University , Xiangtan, 411105 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Xiangtan University , Xiangtan, 411105 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University , Guangzhou 510665 , China"},{"name":"Guangdong Provincial Key Laboratory of Intellectual Property Big Data , Guangzhou 510665 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangzheng","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha, 410000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Xiangtan University , Xiangtan, 411105 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5078-5091","authenticated-orcid":false,"given":"Li","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology , Xiangtan, 411201 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"2023072020160001500_ref1","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.csbj.2021.01.018","article-title":"Review on circular RNAs and new insights into their roles in cancer","volume":"19","author":"Tang","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"2023072020160001500_ref2","doi-asserted-by":"crossref","first-page":"4317","DOI":"10.1038\/s41467-019-11777-7","article-title":"Circular RNA CircFndc3b modulates cardiac repair after myocardial infarction via FUS\/VEGF-A 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