{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:17:00Z","timestamp":1758845820988,"version":"3.44.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20321, 62272490"],"award-info":[{"award-number":["U23A20321, 62272490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hunan Province of China","doi-asserted-by":"crossref","award":["2025JJ20062"],"award-info":[{"award-number":["2025JJ20062"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Transfer RNAs (tRNAs) play critical roles in the process of protein synthesis by decoding messenger RNA codons into amino acids, which is essential for cellular function across various biological pathways and for maintaining metabolic homeostasis. Available evidence implicates that tRNAs are involved in the progression of diverse diseases, underscoring the importance of accurately predicting tRNA\u2013disease associations to understand disease mechanisms and support precision medicine. However, existing methods often struggle with the complexity and heterogeneity inherent in these associations. To address these challenges, we introduce contrastive hypergraph collaborative filtering (CoHGCL), a prediction framework that integrates hypergraph contrastive learning with collaborative filtering. CoHGCL employs graph attention networks to capture local structural features and random walk with restart algorithms to encode global topological patterns. Subsequently, a node-level contrastive learning mechanism alternates between standard graph and hypergraph representations to enhance multiview feature embeddings. These enriched representations are integrated by a collaborative filtering approach through the utilization of generalized matrix factorization for modeling linear associations and multilayer perceptrons for capturing nonlinear interactions. Extensive experimental results on five-fold cross-validation demonstrate that CoHGCL achieves superior performance compared to existing methods, with an area under the receiver operating characteristic curve of 0.9623, area under the precision-recall curve of 0.9430, outperforming all baselines across all metrics. Furthermore, case studies further confirm CoHGCL\u2019s effectiveness in discovering novel and biologically meaningful tRNA\u2013disease associations. The source code and datasets are publicly available at https:\/\/github.com\/Ouyang-cmd\/CoHGCL.<\/jats:p>","DOI":"10.1093\/bib\/bbaf494","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T11:38:43Z","timestamp":1758800323000},"source":"Crossref","is-referenced-by-count":0,"title":["Contrastive hypergraph collaborative filtering for transfer RNA\u2013disease association prediction"],"prefix":"10.1093","volume":"26","author":[{"given":"Tianxiang","family":"Ouyang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University , 666 Shengli Road, Tianshan District, Urumqi, Xinjiang 830046 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