{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:51:19Z","timestamp":1770353479569,"version":"3.49.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62231013"],"award-info":[{"award-number":["62231013"]}],"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":["62250028"],"award-info":[{"award-number":["62250028"]}],"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":["62271329"],"award-info":[{"award-number":["62271329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Provincial Science Fund for Distinguished Young Scholars","award":["2021JDJQ0025"],"award-info":[{"award-number":["2021JDJQ0025"]}]},{"DOI":"10.13039\/100012840","name":"Shenzhen Polytechnic","doi-asserted-by":"publisher","award":["6022310036K"],"award-info":[{"award-number":["6022310036K"]}],"id":[{"id":"10.13039\/100012840","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012840","name":"Shenzhen Polytechnic","doi-asserted-by":"publisher","award":["6023310037K"],"award-info":[{"award-number":["6023310037K"]}],"id":[{"id":"10.13039\/100012840","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Municipal Government of Quzhou","award":["2022D040"],"award-info":[{"award-number":["2022D040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of numerous protein site identification algorithms. Unfortunately, these studies are supervised and require the vast majority of labeled samples in training to produce superior performance. But the acquisition of sample labels requires a large number of biological experiments and is difficult to obtain.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To resolve this matter that a great deal of tags need to be trained in the circRNA-binding site prediction task, a self-supervised learning binding site identification algorithm named CircSI-SSL is proposed in this article. According to the survey, this is unprecedented in the research field. Specifically, CircSI-SSL initially combines multiple feature coding schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised task) to learn mutual information from the multi-view data, and then fine-tuning with only a few sample labels. Comprehensive experiments on six widely used circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent performance in comparison to previous algorithms, even in the extreme case where the ratio of training data to test data is 1:9. In addition, the transplantation experiment of six linRNA datasets without network modification and hyperparameter adjustment shows that CircSI-SSL has good scalability. In summary, the prediction algorithm based on self-supervised learning proposed in this article is expected to replace previous supervised algorithms and has more extensive application value.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and data are available at https:\/\/github.com\/cc646201081\/CircSI-SSL.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae004","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T17:49:51Z","timestamp":1704476991000},"source":"Crossref","is-referenced-by-count":11,"title":["CircSI-SSL: circRNA-binding site identification based on self-supervised learning"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6730-9422","authenticated-orcid":false,"given":"Chao","family":"Cao","sequence":"first","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou, Zhejiang 324003, China"},{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu, Sichuan 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2965-9920","authenticated-orcid":false,"given":"Chunyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China"}]},{"given":"Shuhong","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Guangdong Ocean University , Zhanjiang, Guangdong 524088, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou, Zhejiang 324003, China"},{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu, Sichuan 611731, China"}]}],"member":"286","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"2024011515230619400_btae004-B1","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat Biotechnol"},{"key":"2024011515230619400_btae004-B2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.21037\/ncri.2018.01.02","article-title":"A new method for the 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