{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:09:49Z","timestamp":1776082189867,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"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":["61772381"],"award-info":[{"award-number":["61772381"]}],"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":["62102158"],"award-info":[{"award-number":["62102158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Huazhong Agricultural University Scientific & Technological Self-innovation Foundation"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2662021JC008"],"award-info":[{"award-number":["2662021JC008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2662022JC004"],"award-info":[{"award-number":["2662022JC004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Drug combinations have exhibited promise in treating cancers with less toxicity and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is time-consuming and labor-intensive because of the combinatorial explosion. Although a number of computational methods have been developed for predicting synergistic drug combinations, the multi-way relations between drug combinations and cell lines existing in drug synergy data have not been well exploited.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a multi-way relation-enhanced hypergraph representation learning method to predict anti-cancer drug synergy, named HypergraphSynergy. HypergraphSynergy formulates synergistic drug combinations over cancer cell lines as a hypergraph, in which drugs and cell lines are represented by nodes and synergistic drug\u2013drug\u2013cell line triplets are represented by hyperedges, and leverages the biochemical features of drugs and cell lines as node attributes. Then, a hypergraph neural network is designed to learn the embeddings of drugs and cell lines from the hypergraph and predict drug synergy. Moreover, the auxiliary task of reconstructing the similarity networks of drugs and cell lines is considered to enhance the generalization ability of the model. In the computational experiments, HypergraphSynergy outperforms other state-of-the-art synergy prediction methods on two benchmark datasets for both classification and regression tasks and is applicable to unseen drug combinations or cell lines. The studies revealed that the hypergraph formulation allows us to capture and explain complex multi-way relations of drug combinations and cell lines, and also provides a flexible framework to make the best use of diverse information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source data and codes of HypergraphSynergy can be freely downloaded from https:\/\/github.com\/liuxuan666\/HypergraphSynergy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac579","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T13:36:38Z","timestamp":1661348198000},"page":"4782-4789","source":"Crossref","is-referenced-by-count":84,"title":["Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-9950","authenticated-orcid":false,"given":"Xuan","family":"Liu","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6831-600X","authenticated-orcid":false,"given":"Congzhi","family":"Song","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7217-4462","authenticated-orcid":false,"given":"Shichao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7876-9595","authenticated-orcid":false,"given":"Menglu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}]},{"given":"Xionghui","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"}]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , Wuhan 430070, China"},{"name":"Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Animal Farming Technology, Ministry of Agriculture, Huazhong Agricultural University , Wuhan 430070, China"}]}],"member":"286","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"2022101415200898900_btac579-B1","doi-asserted-by":"crossref","first-page":"107637","DOI":"10.1016\/j.patcog.2020.107637","article-title":"Hypergraph convolution and hypergraph attention","volume":"110","author":"Bai","year":"2021","journal-title":"Pattern Recognit"},{"key":"2022101415200898900_btac579-B2","doi-asserted-by":"crossref","first-page":"i42","DOI":"10.1093\/bioinformatics\/btab336","article-title":"Investigation of refined CNN ensemble learning for anti-cancer drug sensitivity prediction","volume":"37","author":"Bazgir","year":"2021","journal-title":"Bioinformatics"},{"key":"2022101415200898900_btac579-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-45236-6","article-title":"In-silico prediction of synergistic anti-cancer drug combinations using multi-omics data","volume":"9","author":"Celebi","year":"2019","journal-title":"Sci. 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