{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T14:50:12Z","timestamp":1793026212072,"version":"4.5.0"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T00:00:00Z","timestamp":1513296000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>DeepSynergy is available via www.bioinf.jku.at\/software\/DeepSynergy.<\/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\/btx806","type":"journal-article","created":{"date-parts":[[2017,12,14]],"date-time":"2017-12-14T11:12:05Z","timestamp":1513249925000},"page":"1538-1546","source":"Crossref","is-referenced-by-count":663,"title":["DeepSynergy: predicting anti-cancer drug synergy with Deep Learning"],"prefix":"10.1093","volume":"34","author":[{"given":"Kristina","family":"Preuer","sequence":"first","affiliation":[{"name":"Institute of Bioinformatics, Johannes Kepler University, Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard P I","family":"Lewis","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sepp","family":"Hochreiter","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics, Johannes Kepler University, Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna C","family":"Bulusu","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge, UK"},{"name":"Oncology Innovative Medicines and Early Development, AstraZeneca, Hodgkin Building, Chesterford Research Campus, Saffron Walden, Cambs, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00fcnter","family":"Klambauer","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics, Johannes Kepler University, Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,12,15]]},"reference":[{"key":"2023012713021934300_btx806-B1","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1016\/0959-8049(95)00448-3","article-title":"Schedule-dependent interaction between paclitaxel and doxorubicin in human cancer cell lines in vitro","volume":"31","author":"Akutsu","year":"1995","journal-title":"Eur. J. Cancer"},{"key":"2023012713021934300_btx806-B2","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nbt.2284","article-title":"Combinatorial drug therapy for cancer in the post-genomic era","volume":"30","author":"Al-Lazikani","year":"2012","journal-title":"Nat. Biotechnol"},{"key":"2023012713021934300_btx806-B3","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1038\/nrd941","article-title":"Integration of virtual and high-throughput screening","volume":"1","author":"Bajorath","year":"2002","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023012713021934300_btx806-B4","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1038\/nbt.3052","article-title":"A community computational challenge to predict the activity of pairs of compounds","volume":"32","author":"Bansal","year":"2014","journal-title":"Nat. Biotechnol"},{"key":"2023012713021934300_btx806-B5","doi-asserted-by":"crossref","DOI":"10.1186\/s13321-014-0047-1","article-title":"Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation","volume":"6","author":"Baumann","year":"2014","journal-title":"J. Cheminf"},{"key":"2023012713021934300_btx806-B6","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1038\/nrd1086","article-title":"A guide to drug discovery: Hit and lead generation: beyond high-throughput screening","volume":"2","author":"Bleicher","year":"2003","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023012713021934300_btx806-B7","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/j.1744-7348.1939.tb06990.x","article-title":"The toxicity of poisons applied jointly","volume":"26","author":"Bliss","year":"1939","journal-title":"Ann. Appl. Biol"},{"key":"2023012713021934300_btx806-B8","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn"},{"key":"2023012713021934300_btx806-B9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.drudis.2015.09.003","article-title":"Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives","volume":"21","author":"Bulusu","year":"2016","journal-title":"Drug Discov. Today"},{"key":"2023012713021934300_btx806-B10","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1093\/bioinformatics\/btt105","article-title":"ChemoPy: freely available python package for computational biology and chemoinformatics","volume":"29","author":"Cao","year":"2013","journal-title":"Bioinformatics"},{"key":"2023012713021934300_btx806-B11","doi-asserted-by":"crossref","first-page":"e86499.","DOI":"10.1371\/journal.pone.0086499","article-title":"ASDCD: antifungal synergistic drug combination database","volume":"9","author":"Chen","year":"2014","journal-title":"PLoS ONE"},{"key":"2023012713021934300_btx806-B12","doi-asserted-by":"crossref","first-page":"e1004975.","DOI":"10.1371\/journal.pcbi.1004975","article-title":"NLLSS: Predicting synergistic drug combinations based on semi-supervised learning","volume":"12","author":"Chen","year":"2016","journal-title":"PLOS Comput. Biol"},{"key":"2023012713021934300_btx806-B13","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1124\/pr.58.3.10","article-title":"Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies","volume":"58","author":"Chou","year":"2006","journal-title":"Pharmacol. Rev"},{"key":"2023012713021934300_btx806-B14","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1038\/nrd2424","article-title":"The design of drugs for HIV and HCV","volume":"6","author":"Clercq","year":"2007","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023012713021934300_btx806-B15","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn"},{"key":"2023012713021934300_btx806-B16","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.pharmthera.2013.01.016","article-title":"Structure and dynamics of molecular networks: a novel paradigm of drug discovery. Pharmacology and","volume":"138","author":"Csermely","year":"2013","journal-title":"Therapeutics"},{"key":"2023012713021934300_btx806-B17","doi-asserted-by":"crossref","DOI":"10.1186\/s13073-016-0369-x","article-title":"Approaches to modernize the combination drug development paradigm","volume":"8","author":"Day","year":"2016","journal-title":"Genome Med"},{"key":"2023012713021934300_btx806-B18","doi-asserted-by":"crossref","first-page":"2866.","DOI":"10.1093\/bioinformatics\/btw230","article-title":"Combenefit: an interactive platform for the analysis and visualization of drug combinations","volume":"32","author":"Di Veroli","year":"2016","journal-title":"Bioinformatics"},{"key":"2023012713021934300_btx806-B19","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"2023012713021934300_btx806-B20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/wsbm.51","article-title":"Systems approaches and algorithms for discovery of combinatorial therapies","volume":"2","author":"Feala","year":"2010","journal-title":"Wiley Interdiscipl. Rev. Syst. Biol. Med"},{"key":"2023012713021934300_btx806-B21","first-page":"139","volume-title":"Oncogenomics and Cancer Proteomics \u2013 Novel Approaches in Biomarkers Discovery and Therapeutic Targets in Cancer","author":"Ferreira","year":"2013"},{"key":"2023012713021934300_btx806-B22","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat"},{"key":"2023012713021934300_btx806-B23","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1002\/psp4.9","article-title":"A new drug combinatory effect prediction algorithm on the cancer cell based on gene expression and dose-response curve","volume":"4","author":"Goswami","year":"2015","journal-title":"CPT Pharmacometr. Syst. Pharmacol"},{"key":"2023012713021934300_btx806-B24","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1073\/pnas.1311846111","article-title":"High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated b-cell-like diffuse large b-cell lymphoma cells","volume":"111","author":"Griner","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023012713021934300_btx806-B25","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1053\/j.seminhematol.2009.03.003","article-title":"Recent advances in antifungal prevention and treatment","volume":"46","author":"Groll","year":"2009","journal-title":"Semin. Hematol"},{"key":"2023012713021934300_btx806-B26","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1002\/(SICI)1097-0215(19971127)73:5<729::AID-IJC20>3.0.CO;2-#","article-title":"Sequence-dependent activity of the irinotecan-5fu combination in human colon-cancer model ht-29 in vitro and in vivo","volume":"73","author":"Guichard","year":"1997","journal-title":"Int. J. Cancer"},{"key":"2023012713021934300_btx806-B27","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1097\/00001813-200110000-00006","article-title":"Combination of oxaliplatin and irinotecan on human colon cancer cell lines: activity in vitro and in vivo","volume":"12","author":"Guichard","year":"2001","journal-title":"Anti-Cancer Drugs"},{"key":"2023012713021934300_btx806-B28","author":"He","year":"2016"},{"key":"2023012713021934300_btx806-B29","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1200\/JCO.2008.19.8135","article-title":"A randomized phase IIIB trial of chemotherapy, bevacizumab, and panitumumab compared with chemotherapy and bevacizumab alone for metastatic colorectal cancer","volume":"27","author":"Hecht","year":"2009","journal-title":"J. Clin. Oncol"},{"key":"2023012713021934300_btx806-B30","doi-asserted-by":"crossref","first-page":"3.","DOI":"10.1186\/1758-2946-3-3","article-title":"jCompoundMapper: an open source java library and command-line tool for chemical fingerprints","volume":"3","author":"Hinselmann","year":"2011","journal-title":"J. Cheminf"},{"key":"2023012713021934300_btx806-B31","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Magazine"},{"key":"2023012713021934300_btx806-B32","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1093\/bioinformatics\/btl033","article-title":"A new summarization method for affymetrix probe level data","volume":"22","author":"Hochreiter","year":"2006","journal-title":"Bioinformatics"},{"key":"2023012713021934300_btx806-B33","first-page":"705","author":"Huang","year":"2016"},{"key":"2023012713021934300_btx806-B34","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.cell.2016.06.017","article-title":"A landscape of pharmacogenomic interactions in cancer","volume":"166","author":"Iorio","year":"2016","journal-title":"Cell"},{"key":"2023012713021934300_btx806-B35","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/nrd2683","article-title":"Mechanisms of drug combinations: interaction and network perspectives","volume":"8","author":"Jia","year":"2009","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023012713021934300_btx806-B36","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1038\/bjc.1996.425","article-title":"Schedule-dependent interaction between paclitaxel and 5-fluorouracil in human carcinoma cell lines in vitro","volume":"74","author":"Kano","year":"1996","journal-title":"Br. J. Cancer"},{"key":"2023012713021934300_btx806-B37","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s002800050790","article-title":"In vitro schedule-dependent interaction between paclitaxel and SN-38 (the active metabolite of irinotecan) in human carcinoma cell lines","volume":"42","author":"Kano","year":"1998","journal-title":"Cancer Chemother. Pharmacol"},{"key":"2023012713021934300_btx806-B38","first-page":"347","article-title":"Schedule-dependent synergism and antagonism between paclitaxel and methotrexate in human carcinoma cell lines","volume-title":"Oncol. Res.","author":"Kano","year":"1998"},{"key":"2023012713021934300_btx806-B39","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s002800050993","article-title":"Schedule-dependent interactions between paclitaxel and etoposide in human carcinoma cell lines in vitro","volume":"44","author":"Kano","year":"1999","journal-title":"Cancer Chemother. Pharmacol"},{"key":"2023012713021934300_btx806-B40","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/A:1006254315174","article-title":"Schedule-dependent interactions between vinorelbine and paclitaxel in human carcinoma cell lines in vitro","volume":"56","author":"Kano","year":"1999","journal-title":"Breast Cancer Res. Treat"},{"key":"2023012713021934300_btx806-B41","first-page":"972","volume-title":"Adv. Neural. Inf. Process Syst","author":"Klambauer","year":"2017"},{"key":"2023012713021934300_btx806-B42","first-page":"1097","volume-title":"Advances in Neural Information Processing Systems 25","author":"Krizhevsky","year":"2012"},{"key":"2023012713021934300_btx806-B43","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1200\/JCO.2002.12.116","article-title":"Increased oral bioavailability of topotecan in combination with the breast cancer resistance protein and p-glycoprotein inhibitor GF120918","volume":"20","author":"Kruijtzer","year":"2002","journal-title":"J. Clin. Oncol"},{"key":"2023012713021934300_btx806-B44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"2023012713021934300_btx806-B45","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1093\/bioinformatics\/btv080","article-title":"Large-scale exploration and analysis of drug combinations","volume":"31","author":"Li","year":"2015","journal-title":"Bioinformatics"},{"key":"2023012713021934300_btx806-B46","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1093\/bioinformatics\/btp697","article-title":"DCDB: drug combination database","volume":"26","author":"Liu","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012713021934300_btx806-B47","doi-asserted-by":"crossref","first-page":"bau124","DOI":"10.1093\/database\/bau124","article-title":"DCDB 2.0: a major update of the drug combination database","volume":"2014","author":"Liu","year":"2014","journal-title":"Database"},{"key":"2023012713021934300_btx806-B48","first-page":"285","article-title":"The problem of synergism and antagonism of combined drugs","volume":"3","author":"Loewe","year":"1953","journal-title":"Arznei-forschung"},{"key":"2023012713021934300_btx806-B49","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/ci500747n","article-title":"Deep neural nets as a method for quantitative structure\u2013activity relationships","volume":"55","author":"Ma","year":"2015","journal-title":"J. Chem. Inf. Model"},{"key":"2023012713021934300_btx806-B50","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3389\/fenvs.2015.00080","article-title":"DeepTox: toxicity prediction using deep learning","volume":"3","author":"Mayr","year":"2016","journal-title":"Front. Environ. Sci"},{"key":"2023012713021934300_btx806-B51","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1002\/psp4.12104","article-title":"Systematic analysis of quantitative logic model ensembles predicts drug combination effects on cell signaling networks","volume":"5","author":"Morris","year":"2016","journal-title":"CPT Pharmacometri. Syst. Pharmacol"},{"key":"2023012713021934300_btx806-B52","first-page":"807","author":"Nair","year":"2010"},{"key":"2023012713021934300_btx806-B53","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/1758-2946-3-33","article-title":"Open babel: an open chemical toolbox","volume":"3","author":"O\u2019boyle","year":"2011","journal-title":"J. Cheminf"},{"key":"2023012713021934300_btx806-B54","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1158\/1535-7163.MCT-15-0843","article-title":"An unbiased oncology compound screen to identify novel combination strategies","volume":"15","author":"O\u2019neil","year":"2016","journal-title":"Mol. Cancer Therap"},{"key":"2023012713021934300_btx806-B55","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/bib\/bbu010","article-title":"Toward more realistic drug\u2013target interaction predictions","volume":"16","author":"Pahikkala","year":"2015","journal-title":"Brief. Bioinf"},{"key":"2023012713021934300_btx806-B56","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1093\/bioinformatics\/btu046","article-title":"Combinatorial therapy discovery using mixed integer linear programming","volume":"30","author":"Pang","year":"2014","journal-title":"Bioinformatics"},{"key":"2023012713021934300_btx806-B57","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"2023012713021934300_btx806-B58","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model"},{"key":"2023012713021934300_btx806-B59","author":"Sainath","year":"2013"},{"key":"2023012713021934300_btx806-B60","doi-asserted-by":"crossref","DOI":"10.1016\/j.chembiol.2018.01.015","article-title":"Repurposing high-throughput image assays enables biological activity prediction for drug discovery","volume-title":"Cell Chem. Biol.","author":"Simm","year":"2018"},{"key":"2023012713021934300_btx806-B61","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1007\/s00204-015-1587-5","article-title":"Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations","volume":"90","author":"Singh","year":"2016","journal-title":"Arch. Toxicol"},{"key":"2023012713021934300_btx806-B62","doi-asserted-by":"crossref","first-page":"63995","DOI":"10.18632\/oncotarget.11745","article-title":"Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination","volume":"7","author":"Sun","year":"2016","journal-title":"Oncotarget"},{"key":"2023012713021934300_btx806-B63","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1093\/bioinformatics\/btm478","article-title":"I\/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data","volume":"23","author":"Talloen","year":"2007","journal-title":"Bioinformatics"},{"key":"2023012713021934300_btx806-B64","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1128\/CMR.05041-11","article-title":"Combination therapy for treatment of infections with gram-negative bacteria","volume":"25","author":"Tamma","year":"2012","journal-title":"Clin. Microbiol. Rev"},{"key":"2023012713021934300_btx806-B65","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1038\/nbt.2391","article-title":"Systematic identification of synergistic drug pairs targeting HIV","volume":"30","author":"Tan","year":"2012","journal-title":"Nat. Biotechnol"},{"key":"2023012713021934300_btx806-B66","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1056\/NEJMoa0808268","article-title":"Chemotherapy, bevacizumab, and cetuximab in metastatic colorectal cancer","volume":"360","author":"Tol","year":"2009","journal-title":"N. Engl. J. Med"},{"key":"2023012713021934300_btx806-B67","doi-asserted-by":"crossref","first-page":"4425","DOI":"10.1158\/0008-5472.CAN-06-4495","article-title":"Bexarotene (LGD1069, targretin), a selective retinoid x receptor agonist, prevents and reverses gemcitabine resistance in NSCLC cells by modulating gene amplification","volume":"67","author":"Tooker","year":"2007","journal-title":"Cancer Res"},{"key":"2023012713021934300_btx806-B68","author":"Unterthiner","year":"2015"},{"key":"2023012713021934300_btx806-B69","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1146\/annurev.pharmtox.40.1.133","article-title":"High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery","volume":"40","author":"White","year":"2000","journal-title":"Annu. Rev. Pharmacol. Toxicol"},{"key":"2023012713021934300_btx806-B70","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.cels.2015.12.003","article-title":"Prediction of synergism from chemical\u2013genetic interactions by machine learning","volume":"1","author":"Wildenhain","year":"2015","journal-title":"Cell Syst"},{"key":"2023012713021934300_btx806-B71","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.tibtech.2012.12.006","article-title":"Combination approaches to combat multidrug-resistant bacteria","volume":"31","author":"Worthington","year":"2013","journal-title":"Trends Biotechnol"},{"key":"2023012713021934300_btx806-B72","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.csbj.2015.09.001","article-title":"Searching for drug synergy in complex Dose\u2013Response landscapes using an interaction potency model","volume":"13","author":"Yadav","year":"2015","journal-title":"Comput. Struct. Biotechnol. J"},{"key":"2023012713021934300_btx806-B73","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1002\/psp4.1","article-title":"DIGRE: Drug-induced genomic residual effect model for successful prediction of multidrug effects","volume":"4","author":"Yang","year":"2015","journal-title":"CPT Pharmacometr. Syst. Pharmacol"},{"key":"2023012713021934300_btx806-B74","author":"Yu","year":"2016"},{"key":"2023012713021934300_btx806-B75","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/9\/1538\/48915102\/bioinformatics_34_9_1538.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/9\/1538\/48915102\/bioinformatics_34_9_1538.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T08:55:25Z","timestamp":1674809725000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/9\/1538\/4747884"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2017,12,15]]},"references-count":75,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btx806","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,5,1]]},"published":{"date-parts":[[2017,12,15]]}}}