{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T07:24:07Z","timestamp":1760081047501,"version":"3.41.2"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Guangdong University Featured Innovation Program Project","award":["2023KTSCX256"],"award-info":[{"award-number":["2023KTSCX256"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2021YFD1300100"],"award-info":[{"award-number":["2021YFD1300100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Laboratory of Data Space Technology and System","award":["QZQC2024004-6"],"award-info":[{"award-number":["QZQC2024004-6"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976239"],"award-info":[{"award-number":["61976239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Predicting long non-coding RNA (lncRNA)-protein interactions is essential for understanding biological processes and discovering new therapeutic targets. In this study, we propose a novel model based on inter-view contrastive learning and miRNA fusion for lncRNA-protein interaction (LPI) prediction, called ICMF-LPI, which utilizes a heterogeneous information network to enhance LPI prediction. The model integrates miRNA as a mediator, constructing an lncRNA-miRNA-protein network, and employs metapath to extract diverse relationships from heterogeneous graphs. By fusing miRNA-related information and leveraging contrastive learning across inter-views, ICMF-LPI effectively captures potential interactions. Experimental results, including five-fold cross-validation, demonstrate the model\u2019s superior performance compared to several state-of-the-art methods, with significant improvements in the area under the receiver operating characteristic curve and the area under the precision-recall curve metrics. Notably, even when direct LPI connections are excluded, ICMF-LPI still achieves competitive predictive accuracy, performing comparably or better than some existing models. This demonstrates that the proposed model is effective in scenarios where direct interaction data are unavailable. This approach offers a promising direction for developing predictive models in bioinformatics, particularly in challenging conditions.<\/jats:p>","DOI":"10.1093\/bib\/bbaf148","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T02:59:19Z","timestamp":1744081159000},"source":"Crossref","is-referenced-by-count":1,"title":["Inter-view contrastive learning and miRNA fusion for lncRNA-protein interaction prediction in heterogeneous graphs"],"prefix":"10.1093","volume":"26","author":[{"given":"Yijun","family":"Mao","sequence":"first","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University , 483 Wushan Road, Tianhe District, GuangZhou 510642 ,","place":["China"]},{"name":"National Key Laboratory of Data Space Technology and System , 3 Minzhuang Road, Haidian District, Beijing 100195 ,","place":["China"]}]},{"given":"Jiale","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University , 483 Wushan Road, Tianhe District, GuangZhou 510642 ,","place":["China"]}]},{"given":"Jian","family":"Weng","sequence":"additional","affiliation":[{"name":"College of Cyber Security, Jinan University , 601 West Huangpu Avenue, Tianhe District, GuangZhou 510632 ,","place":["China"]}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"College of Cyber Security, Jinan University , 601 West Huangpu Avenue, Tianhe District, GuangZhou 510632 ,","place":["China"]}]},{"given":"Yunyan","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Computer and Infomation Engineering, Guangdong Polytechnic of Industry and Commerce , 1098 North Guangzhou Avenue, Tianhe District, GuangZhou 510510 ,","place":["China"]}]},{"given":"Wanrong","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University , 483 Wushan Road, Tianhe District, GuangZhou 510642 ,","place":["China"]}]},{"given":"Rongjin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Digital process, Wens Foodstuff Group Co., Ltd , 9 Dongdi North Road, Xinxing County, YunFu 527400 ,","place":["China"]}]},{"given":"Rui","family":"Pang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Green Pesticide, College of Plant Protection, South China Agricultural University , 483 Wushan Road, Tianhe District, GuangZhou 510642 ,","place":["China"]}]},{"given":"Xudong","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University , 483 Wushan Road, Tianhe District, GuangZhou 510642 ,","place":["China"]}]},{"given":"Deyu","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University , 483 Wushan Road, Tianhe District, GuangZhou 510642 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