{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T08:51:53Z","timestamp":1782982313842,"version":"3.54.5"},"reference-count":72,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T00:00:00Z","timestamp":1642550400000},"content-version":"vor","delay-in-days":0,"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":["2017YFC0908500"],"award-info":[{"award-number":["2017YFC0908500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC1303205"],"award-info":[{"award-number":["2016YFC1303205"]}],"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":["31970638"],"award-info":[{"award-number":["31970638"]}],"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":["61572361"],"award-info":[{"award-number":["61572361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Natural Science Foundation Program","award":["17ZR1449400"],"award-info":[{"award-number":["17ZR1449400"]}]},{"name":"Shanghai Artificial Intelligence Technology Standard Project","award":["19DZ2200900"],"award-info":[{"award-number":["19DZ2200900"]}]},{"name":"Major Program of Development Fund for Shanghai Zhangjiang National Innovation Demonstration Zone","award":["ZJ2018-ZD-004"],"award-info":[{"award-number":["ZJ2018-ZD-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein\u2013protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision\u2013recall curve of 0.63 and a Cohen\u2019s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network\u2019s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.<\/jats:p>","DOI":"10.1093\/bib\/bbab587","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T12:12:39Z","timestamp":1640261559000},"source":"Crossref","is-referenced-by-count":80,"title":["PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein\u2013protein interaction network"],"prefix":"10.1093","volume":"23","author":[{"given":"Xiaowen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongming","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, 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