{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:59:21Z","timestamp":1784185161178,"version":"3.55.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"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":["61701073"],"award-info":[{"award-number":["61701073"]}],"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":["62031003"],"award-info":[{"award-number":["62031003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"High-level Talent Innovation Support Program of Dalian City","award":["2020RQ059"],"award-info":[{"award-number":["2020RQ059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs. Results: We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA\u2013disease associations. Firstly, the circRNA\u2013circRNA similarity network and disease\u2013disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA\u2013disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA\u2013disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.<\/jats:p>","DOI":"10.1093\/bib\/bbac379","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T21:58:26Z","timestamp":1662587906000},"source":"Crossref","is-referenced-by-count":47,"title":["GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs"],"prefix":"10.1093","volume":"23","author":[{"given":"Qiguo","family":"Dai","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering , , 116600, Dalian , China"},{"name":"Dalian Minzu University , , 116600, Dalian , China"},{"name":"SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University , 116600, Dalian , 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