{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:10:36Z","timestamp":1759205436531,"version":"3.37.3"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"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":["61822306"],"award-info":[{"award-number":["61822306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018AAA0100100"],"award-info":[{"award-number":["2018AAA0100100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["JQ19019"],"award-info":[{"award-number":["JQ19019"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Due to the inherent stability and close relationship with the progression of diseases, circRNAs are serving as important biomarkers and drug targets. Efficient predictors for identifying circRNA\u2013disease associations are highly required. The existing predictors consider circRNA\u2013disease association prediction as a classification task or a recommendation problem, failing to capture the ranking information among the associations and detect the diseases associated with new circRNAs. However, more and more circRNAs are discovered. Identification of the diseases associated with these new circRNAs remains a challenging task.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we proposed a new predictor called iCricDA-LTR for circRNA\u2013disease association prediction. Different from any existing predictor, iCricDA-LTR employed a ranking framework to model the global ranking associations among the query circRNAs and the diseases. The Learning to Rank (LTR) algorithm was employed to rank the associations based on various predictors and features in a supervised manner. The experimental results on two independent test datasets showed that iCircDA-LTR outperformed the other competing methods, especially for predicting the diseases associated with new circRNAs. As a result, iCircDA-LTR is more suitable for the real-world applications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>For the convenience of researchers to detect new circRNA\u2013disease associations. The web server of iCircDA-LTR was established and freely available at http:\/\/bliulab.net\/iCircDA-LTR\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab334","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T21:27:52Z","timestamp":1620163672000},"page":"3302-3310","source":"Crossref","is-referenced-by-count":26,"title":["iCircDA-LTR: identification of circRNA\u2013disease associations based on Learning to Rank"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-1716","authenticated-orcid":false,"given":"Hang","family":"Wei","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen, Guangdong 518055, China"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen, Guangdong 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3685-9469","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen, Guangdong 518055, China"},{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing 100081, China"},{"name":"Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology , Beijing 100081, China"}]}],"member":"286","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"key":"2023051608271355800_btab334-B1","doi-asserted-by":"crossref","first-page":"R90","DOI":"10.1186\/gb-2010-11-8-r90","article-title":"Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites","volume":"11","author":"Betel","year":"2010","journal-title":"Genome Biol"},{"key":"2023051608271355800_btab334-B2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. 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