{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T18:20:21Z","timestamp":1774376421251,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012012","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000}}],"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"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":["61971422"],"award-info":[{"award-number":["61971422"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xuzhou Science and Technology Innovation Plan - Key Special Project for Social Development","award":["KC22112"],"award-info":[{"award-number":["KC22112"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012012","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T17:56:46Z","timestamp":1712253406000},"page":"e1012012","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction"],"prefix":"10.1371","volume":"20","author":[{"given":"Hui","family":"Liu","sequence":"first","affiliation":[]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Chaoju","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7404-5231","authenticated-orcid":true,"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"pcbi.1012012.ref001","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1038\/nature12625","article-title":"The causes and consequences of genetic heterogeneity in cancer evolution","volume":"501","author":"RA Burrell","year":"2013","journal-title":"Nature"},{"key":"pcbi.1012012.ref002","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1158\/2159-8290.CD-16-1154","article-title":"Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine.","volume":"7","author":"C Pauli","year":"2017","journal-title":"Cancer Discov"},{"key":"pcbi.1012012.ref003","doi-asserted-by":"crossref","first-page":"4781","DOI":"10.3390\/ijms20194781","article-title":"The Need for Multi-Omics Biomarker Signatures in Precision Medicine.","volume":"20","author":"M Olivier","year":"2019","journal-title":"Int J Mol Sci."},{"key":"pcbi.1012012.ref004","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s12532-012-0044-1","article-title":"Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm","volume":"4","author":"Z Wen","year":"2012","journal-title":"Math Prog Comp"},{"key":"pcbi.1012012.ref005","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1021\/ci500152b","article-title":"Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization.","volume":"54","author":"M Ammad-ud-din","year":"2014","journal-title":"J Chem Inf Model"},{"key":"pcbi.1012012.ref006","doi-asserted-by":"crossref","first-page":"513","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.1012012.ref007","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.1093\/bioinformatics\/bty452","article-title":"Predicting Cancer Drug Response using a Recommender System. Wren J, editor","volume":"34","author":"C Suphavilai","year":"2018","journal-title":"Bioinformatics"},{"key":"pcbi.1012012.ref008","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.omtn.2019.05.017","article-title":"Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.","volume":"17","author":"N-N Guan","year":"2019","journal-title":"Molecular Therapy\u2014Nucleic Acids"},{"key":"pcbi.1012012.ref009","doi-asserted-by":"crossref","first-page":"411","DOI":"10.2174\/1574893617666220302123118","article-title":"SEMCM: A Self-Expressive Matrix Completion Model for Anti-cancerDrug Sensitivity Prediction.","volume":"17","author":"L Zhang","year":"2022","journal-title":"Curr Bioinform"},{"key":"pcbi.1012012.ref010","doi-asserted-by":"crossref","first-page":"835","DOI":"10.2174\/1574893617666220609114052","article-title":"NeuMF: Predicting Anti-cancer Drug Response Through a Neural MatrixFactorization Model.","volume":"17","author":"H Liu","year":"2022","journal-title":"Curr Bioinform."},{"key":"pcbi.1012012.ref011","doi-asserted-by":"crossref","DOI":"10.1145\/1970392.1970395","article-title":"Robust principal component analysis","volume":"58","author":"EJ Cand\u00e8s","year":"2011","journal-title":"J ACM"},{"key":"pcbi.1012012.ref012","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.1012012.ref013","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1038\/s41467-021-21997-5","article-title":"Deep generative neural network for accurate drug response imputation.","volume":"12","author":"P Jia","year":"2021","journal-title":"Nat Commun"},{"key":"pcbi.1012012.ref014","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1093\/bioinformatics\/btab650","article-title":"TGSA: protein\u2013protein association-based twin graph neural networks for drug response prediction with similarity augmentation","volume":"38","author":"Y Zhu","year":"2022","journal-title":"Bioinformatics"},{"key":"pcbi.1012012.ref015","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":"2022","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"pcbi.1012012.ref016","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"},{"key":"pcbi.1012012.ref017","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1006\/jmps.1999.1279","article-title":"Cross-Validation Methods","volume":"44","author":"MW Browne","year":"2000","journal-title":"Journal of Mathematical Psychology"},{"key":"pcbi.1012012.ref018","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1111\/stan.12111","article-title":"Analytic posteriors for Pearson\u2019s correlation coefficient.","volume":"72","author":"A Ly","year":"2018","journal-title":"Stat Neerl"},{"key":"pcbi.1012012.ref019","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/0022-2836(80)90289-2","article-title":"On the prediction of protein structure: The significance of the root-mean-square deviation","volume":"138","author":"FE Cohen","year":"1980","journal-title":"J Mol Biol"},{"key":"pcbi.1012012.ref020","first-page":"426","article-title":"Factorization meets the neighborhood: a multifaceted collaborative filtering model. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY","author":"Y. Koren","year":"2008","journal-title":"USA: Association for Computing Machinery"},{"key":"pcbi.1012012.ref021","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1038\/nrc839","article-title":"The phosphatidylinositol 3-Kinase\u2013AKT pathway in human cancer","volume":"2","author":"I Vivanco","year":"2002","journal-title":"Nat Rev Cancer"},{"key":"pcbi.1012012.ref022","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1038\/nrd2926","article-title":"Targeting the phosphoinositide 3-kinase pathway in cancer","volume":"8","author":"P Liu","year":"2009","journal-title":"Nat Rev Drug Discov"},{"key":"pcbi.1012012.ref023","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1038\/nrd4204","article-title":"PI3K and cancer: lessons, challenges and opportunities.","volume":"13","author":"DA Fruman","year":"2014","journal-title":"Nat Rev Drug Discov."},{"key":"pcbi.1012012.ref024","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1056\/NEJMoa066838","article-title":"Temsirolimus, Interferon Alfa, or Both for Advanced Renal-Cell Carcinoma","volume":"356","author":"G Hudes","year":"2007","journal-title":"New England Journal of Medicine"},{"key":"pcbi.1012012.ref025","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1038\/nm0202-128","article-title":"Rapamycin inhibits primary and metastatic tumor growth by antiangiogenesis: involvement of vascular endothelial growth factor","volume":"8","author":"M Guba","year":"2002","journal-title":"Nat Med"},{"key":"pcbi.1012012.ref026","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1158\/1535-7163.MCT-10-1099","article-title":"Preclinical Characterization of OSI-027, a Potent and Selective Inhibitor of mTORC1 and mTORC2: Distinct from Rapamycin","volume":"10","author":"SV Bhagwat","year":"2011","journal-title":"Molecular Cancer Therapeutics"},{"key":"pcbi.1012012.ref027","first-page":"13","article-title":"Retinoblastoma Protein Paralogs and Tumor Suppression.","author":"M Flores","year":"2022","journal-title":"Frontiers in Genetics"},{"key":"pcbi.1012012.ref028","doi-asserted-by":"crossref","first-page":"174864","DOI":"10.1016\/j.ejphar.2022.174864","article-title":"Fracture repair by IOX2: Regulation of the hypoxia inducible factor-1\u03b1 signaling pathway and BMSCs","volume":"921","author":"C Chen","year":"2022","journal-title":"Eur J Pharmacol"},{"key":"pcbi.1012012.ref029","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1158\/1078-0432.CCR-13-1607","article-title":"Inhibition of DNA double-strand break repair by the dual PI3K\/mTOR inhibitor NVP-BEZ235 as a strategy for radiosensitization of glioblastoma","volume":"20","author":"CR Gil del Alcazar","year":"2014","journal-title":"Clin Cancer Res"},{"key":"pcbi.1012012.ref030","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1073\/pnas.0711741105","article-title":"Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity","volume":"105","author":"J Tsai","year":"2008","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1012012.ref031","doi-asserted-by":"crossref","first-page":"15544","DOI":"10.1016\/S0021-9258(18)66748-1","article-title":"Overexpression of a novel anionic glutathione transferase in multidrug-resistant human breast cancer cells","volume":"261","author":"G Batist","year":"1986","journal-title":"J Biol Chem"},{"key":"pcbi.1012012.ref032","first-page":"511","article-title":"The Ras-Raf-MEK-ERK pathway in the treatment of cancer","volume":"25","author":"RA Hilger","year":"2002","journal-title":"Onkologie"},{"key":"pcbi.1012012.ref033","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.bbamcr.2006.10.001","article-title":"Roles of the Raf\/MEK\/ERK pathway in cell growth, malignant transformation and drug resistance","volume":"1773","author":"JA McCubrey","year":"2007","journal-title":"Biochim Biophys Acta"},{"key":"pcbi.1012012.ref034","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview.","volume":"61","author":"J. Schmidhuber","year":"2015","journal-title":"Neural Networks."},{"key":"pcbi.1012012.ref035","unstructured":"Igel C, H\u00fcsken M. Improving the Rprop learning algorithm. In Proceedings of the Second International Symposium on Neural Computation, NC\u20192000. ICSC Academic Press. 2000. p. 115\u2013121"},{"key":"pcbi.1012012.ref036","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0925-2312(01)00700-7","article-title":"Empirical evaluation of the improved Rprop learning algorithms.","volume":"50","author":"C Igel","year":"2003","journal-title":"Neurocomputing."},{"key":"pcbi.1012012.ref037","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"YA LeCun","year":"2012","edition":"2"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1012012","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T18:32:41Z","timestamp":1713292361000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012012"}},"subtitle":[],"editor":[{"given":"James","family":"Gallo","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,4,4]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4,4]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1012012","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1012012","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,4]]}}}