{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T01:02:33Z","timestamp":1775437353067,"version":"3.50.1"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0102"],"award-info":[{"award-number":["2021SHZDZX0102"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072206"],"award-info":[{"award-number":["62072206"]}],"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":["62172273"],"award-info":[{"award-number":["62172273"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.<\/jats:p>","DOI":"10.1093\/bib\/bbac403","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T16:33:56Z","timestamp":1663864436000},"source":"Crossref","is-referenced-by-count":35,"title":["Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8780-0892","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University , Shanghai 200240, China"}]},{"given":"Shikui","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University , Shanghai 200240, China"}]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of informatics, Huazhong Agricultural University , Wuhan 430070, China"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University , Shanghai 200240, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"2022112111111459000_ref1","doi-asserted-by":"crossref","first-page":"6142","DOI":"10.1111\/febs.15788","article-title":"Druggable genome and precision medicine in cancer: current challenges","volume":"288","author":"Dupont","year":"2021","journal-title":"FEBS 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