{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:12:37Z","timestamp":1781032357516,"version":"3.54.1"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFE0204200"],"award-info":[{"award-number":["2023YFE0204200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20387"],"award-info":[{"award-number":["U20A20387"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA\u2013disease associations (MDAs), miRNA\u2013target interactions (MTIs), and disease\u2013gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https:\/\/github.com\/Sue-syx\/GlaHGCL.<\/jats:p>","DOI":"10.1093\/bib\/bbae443","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T01:43:39Z","timestamp":1726019019000},"source":"Crossref","is-referenced-by-count":6,"title":["Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA\u2013gene\u2013disease networks"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2828-5231","authenticated-orcid":false,"given":"Yuxuan","family":"Si","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine , East Qingchun Road, 310016 Zhejiang, China"},{"name":"College of Computer Science and Technology, Zhejiang University , 38 Zheda Road, 310027 Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University , 38 Zheda Road, 310027 Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengqing","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine , East Qingchun Road, 310016 Zhejiang, China"},{"name":"College of Computer Science and Technology, Zhejiang University , 38 Zheda Road, 310027 Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhouhang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine , East Qingchun Road, 310016 Zhejiang, China"},{"name":"College of Computer Science and Technology, Zhejiang University , 38 Zheda Road, 310027 Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxing","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University , 38 Zheda Road, 310027 Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingming","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University , 38 Zheda Road, 310027 Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University , 38 Zheda Road, 310027 Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University , 38 Zheda Road, 310027 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