{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T13:14:29Z","timestamp":1774185269473,"version":"3.50.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":17,"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":["32170680"],"award-info":[{"award-number":["32170680"]}],"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":["T2122018"],"award-info":[{"award-number":["T2122018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shanghai","award":["21ZR1476000"],"award-info":[{"award-number":["21ZR1476000"]}]},{"name":"CAS Youth Innovation Promotion Association","award":["Y2022076"],"award-info":[{"award-number":["Y2022076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and makes complementary contributes to the final prediction of the drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code and data are available at https:\/\/github.com\/LiHongCSBLab\/JointSyn.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae604","type":"journal-article","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T08:47:40Z","timestamp":1729154860000},"source":"Crossref","is-referenced-by-count":14,"title":["Dual-view jointly learning improves personalized drug synergy prediction"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7726-8298","authenticated-orcid":false,"given":"Xueliang","family":"Li","sequence":"first","affiliation":[{"name":"CAS Key Laboratory of Computational Biology, 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Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031,","place":["China"]}]},{"given":"Zhixuan","family":"Tang","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9103-4913","authenticated-orcid":false,"given":"Liangxiao","family":"Ma","sequence":"additional","affiliation":[{"name":"Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Science , Shanghai 200031,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5679-9980","authenticated-orcid":false,"given":"Hong","family":"Li","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Computational Biology, 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