{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T14:13:30Z","timestamp":1780582410030,"version":"3.54.1"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":50,"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":["U23A20321"],"award-info":[{"award-number":["U23A20321"]}],"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":["62272490"],"award-info":[{"award-number":["62272490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug\u2013gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models\u2019 state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug\u2013gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA\u2019s superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA\u2019s potential in discovering new drug\u2013gene associations. The code and dataset for SGCLDGA are freely available at https:\/\/github.com\/one-melon\/SGCLDGA.<\/jats:p>","DOI":"10.1093\/bib\/bbae231","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T14:20:56Z","timestamp":1715869256000},"source":"Crossref","is-referenced-by-count":16,"title":["SGCLDGA: unveiling drug\u2013gene associations through simple graph contrastive learning"],"prefix":"10.1093","volume":"25","author":[{"given":"Yanhao","family":"Fan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , 410075, Changsha , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Che","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of software, Xinjiang University , 830046, Urumqi , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowen","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , 410075, Changsha , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijian","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , 410075, Changsha , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiameng","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , 410075, Changsha , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , 410075, Changsha , 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