{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:09:53Z","timestamp":1783728593420,"version":"3.55.0"},"reference-count":55,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306206"],"award-info":[{"award-number":["62306206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013317","name":"Key Research and Development Program of Shanxi Province","doi-asserted-by":"crossref","award":["202102020101007"],"award-info":[{"award-number":["202102020101007"]}],"id":[{"id":"10.13039\/501100013317","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Applied Basic Research Program of Shanxi Province","award":["202203021212207"],"award-info":[{"award-number":["202203021212207"]}]},{"name":"University-level Youth Fund of Taiyuan University of Technology","award":["No.2022QN041"],"award-info":[{"award-number":["No.2022QN041"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs. The core of ASGCL is the GraphMorpher module, an innovative component that enhances the input graph structure via strategic node attribute masking and topological pruning. By contrasting the augmented graph with the original input, the model delineates distinct positive and negative sample sets at both node and graph levels. This dual-level contrastive approach significantly amplifies the model\u2019s discriminatory prowess in identifying nuanced drug responses. Leveraging a synergistic combination of supervised and contrastive loss, ASGCL accomplishes end-to-end learning of feature representations, substantially outperforming existing methodologies. Comprehensive ablation studies underscore the efficacy of each component, corroborating the model\u2019s robustness. Experimental evaluations further illuminate ASGCL\u2019s proficiency in predicting drug responses, offering a potent tool for guiding clinical decision-making in cancer therapy.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012748","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T18:46:22Z","timestamp":1738262782000},"page":"e1012748","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction"],"prefix":"10.1371","volume":"21","author":[{"given":"Yunyun","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanrong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6772-4247","authenticated-orcid":true,"given":"Yuhua","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6820-4810","authenticated-orcid":true,"given":"Ziting","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiufang","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"issue":"7391","key":"pcbi.1012748.ref001","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1038\/nature11005","article-title":"Systematic identification of genomic markers of drug sensitivity in cancer cells","volume":"483","author":"MJ Garnett","year":"2012","journal-title":"Nature"},{"issue":"4","key":"pcbi.1012748.ref002","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/JBHI.2015.2412522","article-title":"Optimal drug prediction from personal genomics profiles","volume":"19","author":"J Sheng","year":"2015","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"pcbi.1012748.ref003","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1093\/bib\/bbz171","article-title":"Deep learning for drug response prediction in cancer","volume":"22","author":"D Baptista","year":"2021","journal-title":"Brief Bioinform"},{"issue":"D1","key":"pcbi.1012748.ref004","doi-asserted-by":"crossref","first-page":"D955","DOI":"10.1093\/nar\/gks1111","article-title":"Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells","volume":"41","author":"W Yang","year":"2012","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"pcbi.1012748.ref005","doi-asserted-by":"crossref","first-page":"e0212108","DOI":"10.1371\/journal.pone.0212108","article-title":"Drug sensitivity prediction with high-dimensional mixture regression","volume":"14","author":"Q Li","year":"2019","journal-title":"PLoS One"},{"issue":"7391","key":"pcbi.1012748.ref006","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"J Barretina","year":"2012","journal-title":"Nature"},{"issue":"1","key":"pcbi.1012748.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12885-017-3500-5","article-title":"Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization","volume":"17","author":"L Wang","year":"2017","journal-title":"BMC Cancer"},{"key":"pcbi.1012748.ref008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2014-15-3-r47","article-title":"Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines","volume":"15","author":"P Geeleher","year":"2014","journal-title":"Genome Biol"},{"issue":"1","key":"pcbi.1012748.ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12885-015-1492-6","article-title":"Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection","volume":"15","author":"Z Dong","year":"2015","journal-title":"BMC Cancer"},{"key":"pcbi.1012748.ref010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2013-14-10-r110","article-title":"Modeling precision treatment of breast cancer","volume":"14","author":"A Daemen","year":"2013","journal-title":"Genome Biol"},{"key":"pcbi.1012748.ref011","first-page":"1","article-title":"Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces","author":"Z Xia","year":"2010","journal-title":"BMC systems biology, BioMed Central"},{"issue":"13","key":"pcbi.1012748.ref012","doi-asserted-by":"crossref","first-page":"4181","DOI":"10.1093\/nar\/gkp552","article-title":"Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances","volume":"37","author":"T LaFramboise","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1012748.ref013","first-page":"217","volume-title":"Annual reports in computational chemistry","author":"EE Bolton","year":"2008"},{"key":"pcbi.1012748.ref014","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","article-title":"Molecular graph convolutions: moving beyond fingerprints","volume":"30","author":"S Kearnes","year":"2016","journal-title":"J Comput Aided Mol Des"},{"issue":"14","key":"pcbi.1012748.ref015","doi-asserted-by":"crossref","first-page":"i501","DOI":"10.1093\/bioinformatics\/btz318","article-title":"MOLI: multi-omics late integration with deep neural networks for drug response prediction","volume":"35","author":"H Sharifi-Noghabi","year":"2019","journal-title":"Bioinformatics"},{"key":"pcbi.1012748.ref016","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ymeth.2019.02.009","article-title":"Deep-Resp-Forest: a deep forest model to predict anti-cancer drug response","volume":"166","author":"R Su","year":"2019","journal-title":"Methods"},{"issue":"9","key":"pcbi.1012748.ref017","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1093\/bioinformatics\/btw765","article-title":"IntegratedMRF: random forest-based framework for integrating prediction from different data types","volume":"33","author":"R Rahman","year":"2017","journal-title":"Bioinformatics"},{"issue":"2","key":"pcbi.1012748.ref018","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1158\/1541-7786.MCR-17-0378","article-title":"Precision oncology beyond targeted therapy: combining omics data with machine learning matches the majority of cancer cells to effective therapeutics","volume":"16","author":"MQ Ding","year":"2018","journal-title":"Molecular cancer research"},{"issue":"9","key":"pcbi.1012748.ref019","doi-asserted-by":"crossref","first-page":"e1004498","DOI":"10.1371\/journal.pcbi.1004498","article-title":"Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model","volume":"11","author":"N Zhang","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"pcbi.1012748.ref020","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/TCBB.2021.3060430","article-title":"Graph convolutional networks for drug response prediction","volume":"19","author":"T Nguyen","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"Suppl","key":"pcbi.1012748.ref021","doi-asserted-by":"crossref","first-page":"i911","DOI":"10.1093\/bioinformatics\/btaa822","article-title":"DeepCDR: a hybrid graph convolutional network for predicting cancer drug response","volume":"36","author":"Q Liu","year":"2020","journal-title":"Bioinformatics"},{"issue":"2","key":"pcbi.1012748.ref022","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1109\/TCBB.2019.2919581","article-title":"DeepDSC: a deep learning method to predict drug sensitivity of cancer cell lines","volume":"18","author":"M Li","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"pcbi.1012748.ref023","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1038\/s41598-020-58821-x","article-title":"RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance","volume":"10","author":"J Choi","year":"2020","journal-title":"Sci Rep"},{"issue":"1","key":"pcbi.1012748.ref024","first-page":"1","article-title":"Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network","volume":"20","author":"P Liu","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1012748.ref025","doi-asserted-by":"crossref","unstructured":"Li Q, Huang J, Zhu H, Liu Q, \u201cPrediction of cancer drug effectiveness based on multi-fusion deep learning model,\u201d in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, 2020, pp. 634\u2013639.","DOI":"10.1109\/CCWC47524.2020.9031163"},{"key":"pcbi.1012748.ref026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12918-017-0463-8","article-title":"A link prediction approach to cancer drug sensitivity prediction","volume":"11","author":"T Turki","year":"2017","journal-title":"BMC Syst Biol"},{"issue":"1","key":"pcbi.1012748.ref027","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1038\/s41598-018-21622-4","article-title":"A novel heterogeneous network-based method for drug response prediction in cancer cell lines","volume":"8","author":"F Zhang","year":"2018","journal-title":"Sci Rep"},{"key":"pcbi.1012748.ref028","doi-asserted-by":"crossref","unstructured":"Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L, \u201cGraph contrastive learning with adaptive augmentation,\u201d in Proceedings of the Web Conference 2021, 2021, pp. 2069\u20132080.","DOI":"10.1145\/3442381.3449802"},{"key":"pcbi.1012748.ref029","first-page":"30414","article-title":"Infogcl: Information-aware graph contrastive learning","volume":"34","author":"D Xu","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"pcbi.1012748.ref030","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G, \u201cA simple framework for contrastive learning of visual representations,\u201d in International conference on machine learning, PMLR, 2020, pp. 1597\u20131607."},{"key":"pcbi.1012748.ref031","first-page":"10341","article-title":"Deep graph infomax","author":"P Veli\u010dkovi\u0107","year":"2018","journal-title":"arXiv preprint arXiv:1809"},{"key":"pcbi.1012748.ref032","doi-asserted-by":"crossref","unstructured":"Qiu J, Chen Q, Dong Y, Zhang J, Yang H, Ding M et al., \u201cGcc: Graph contrastive coding for graph neural network pre-training,\u201d in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020, pp. 1150\u20131160.","DOI":"10.1145\/3394486.3403168"},{"issue":"1","key":"pcbi.1012748.ref033","doi-asserted-by":"crossref","first-page":"bbab457","DOI":"10.1093\/bib\/bbab457","article-title":"GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction","volume":"23","author":"X Liu","year":"2022","journal-title":"Brief Bioinform"},{"issue":"1","key":"pcbi.1012748.ref034","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Y Luo","year":"2017","journal-title":"Nat Commun"},{"issue":"2","key":"pcbi.1012748.ref035","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1093\/bioinformatics\/btab651","article-title":"MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks","volume":"38","author":"H Fu","year":"2022","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1012748.ref036","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1186\/s12885-021-08359-6","article-title":"Predicting breast cancer drug response using a multiple-layer cell line drug response network model","volume":"21","author":"S Huang","year":"2021","journal-title":"BMC Cancer"},{"issue":"17","key":"pcbi.1012748.ref037","doi-asserted-by":"crossref","first-page":"i455","DOI":"10.1093\/bioinformatics\/btw433","article-title":"Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization","volume":"32","author":"M Ammad-Ud-Din","year":"2016","journal-title":"Bioinformatics"},{"issue":"22","key":"pcbi.1012748.ref038","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.1093\/bioinformatics\/bty452","article-title":"Predicting cancer drug response using a recommender system","volume":"34","author":"C Suphavilai","year":"2018","journal-title":"Bioinformatics"},{"key":"pcbi.1012748.ref039","first-page":"2016","article-title":"Semi-supervised classification with graph convolutional networks","author":"TN Kipf","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"pcbi.1012748.ref040","first-page":"10903","article-title":"Graph attention networks","author":"P Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv preprint arXiv:1710"},{"issue":"19","key":"pcbi.1012748.ref041","doi-asserted-by":"crossref","first-page":"4546","DOI":"10.1093\/bioinformatics\/btac574","article-title":"Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions","volume":"38","author":"W Peng","year":"2022","journal-title":"Bioinformatics"},{"issue":"3","key":"pcbi.1012748.ref042","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1109\/JBHI.2021.3102186","article-title":"Predicting drug response based on multi-omics fusion and graph convolution","volume":"26","author":"W Peng","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"pcbi.1012748.ref043","doi-asserted-by":"crossref","first-page":"107199","DOI":"10.1016\/j.compbiomed.2023.107199","article-title":"Semi-supervised heterogeneous graph contrastive learning for drug\u2013target interaction prediction","volume":"163","author":"K Yao","year":"2023","journal-title":"Comput Biol Med"},{"issue":"1","key":"pcbi.1012748.ref044","first-page":"857","article-title":"Self-supervised learning: Generative or contrastive","volume":"35","author":"X Liu","year":"2021","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"pcbi.1012748.ref045","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1093\/bioinformatics\/btac164","article-title":"Supervised graph co-contrastive learning for drug\u2013target interaction prediction","volume":"38","author":"Y Li","year":"2022","journal-title":"Bioinformatics"},{"key":"pcbi.1012748.ref046","doi-asserted-by":"crossref","unstructured":"Wang Y, Min Y, Chen X, Wu J, \u201cMulti-view graph contrastive representation learning for drug-drug interaction prediction,\u201d in Proceedings of the Web Conference 2021, 2021, pp. 2921\u20132933.","DOI":"10.1145\/3442381.3449786"},{"key":"pcbi.1012748.ref047","doi-asserted-by":"crossref","first-page":"119139","DOI":"10.1016\/j.ins.2023.119139","article-title":"Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reaction","volume":"642","author":"L Zhuang","year":"2023","journal-title":"Inf Sci (N Y)"},{"key":"pcbi.1012748.ref048","doi-asserted-by":"crossref","unstructured":"Xu R, Luo L, Liu Z, Ouyang C, Wan Y, \u201cCLDDI: A Novel Method for Predicting Drug-Drug Interaction Events Based on Graph Contrastive Learning,\u201d in 2023 11th International Conference on Bioinformatics and Computational Biology (ICBCB), IEEE, 2023, pp. 105\u2013112.","DOI":"10.1109\/ICBCB57893.2023.10246685"},{"issue":"4","key":"pcbi.1012748.ref049","doi-asserted-by":"crossref","first-page":"bbad247","DOI":"10.1093\/bib\/bbad247","article-title":"GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning","volume":"24","author":"J Wei","year":"2023","journal-title":"Brief Bioinform"},{"key":"pcbi.1012748.ref050","doi-asserted-by":"crossref","first-page":"110120","DOI":"10.1016\/j.knosys.2022.110120","article-title":"SMGCL: Semi-supervised Multi-view Graph Contrastive Learning","volume":"260","author":"H Zhou","year":"2023","journal-title":"Knowl Based Syst"},{"issue":"2","key":"pcbi.1012748.ref051","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1038\/leu.2017.211","article-title":"Bortezomib before and after high-dose therapy in myeloma: long-term results from the phase III HOVON-65\/GMMG-HD4 trial","volume":"32","author":"H Goldschmidt","year":"2018","journal-title":"Leukemia"},{"key":"pcbi.1012748.ref052","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"Y You","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"pcbi.1012748.ref053","article-title":"Graph condensation for graph neural networks","author":"W Jin","year":"2021","journal-title":"arXiv preprint arXiv:2110.07580"},{"issue":"5","key":"pcbi.1012748.ref054","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1086\/228631","article-title":"Power and centrality: A family of measures","volume":"92","author":"P Bonacich","year":"1987","journal-title":"American journal of sociology"},{"key":"pcbi.1012748.ref055","first-page":"161","article-title":"The PageRank citation ranking: bringing order to the web","volume":"98","author":"S Brin","year":"1998","journal-title":"Proceedings of ASIS"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012748","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T18:46:45Z","timestamp":1738262805000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012748"}},"subtitle":[],"editor":[{"given":"\u00d6zlem","family":"Tastan Bishop","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,1,30]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1,30]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1012748","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,30]]}}}