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dc.contributor.authorPreuer, Kristina
dc.contributor.authorLewis, Richard PI
dc.contributor.authorHochreiter, Sepp
dc.contributor.authorBender, Andreas
dc.contributor.authorBulusu, Krishna C
dc.contributor.authorKlambauer, Günter
dc.date.accessioned2018-11-06T00:30:15Z
dc.date.available2018-11-06T00:30:15Z
dc.date.issued2018-05-01
dc.identifier.issn1367-4803
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284638
dc.description.abstractMotivation: 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. Results: 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. Availability and implementation: DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Contact: klambauer@bioinf.jku.at. Supplementary information: Supplementary data are available at Bioinformatics online.
dc.format.mediumPrint
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCell Line, Tumor
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectAntineoplastic Combined Chemotherapy Protocols
dc.subjectGene Expression Profiling
dc.subjectComputational Biology
dc.subjectGene Expression Regulation, Neoplastic
dc.subjectSoftware
dc.subjectSupport Vector Machine
dc.subjectDeep Learning
dc.titleDeepSynergy: predicting anti-cancer drug synergy with Deep Learning.
dc.typeArticle
prism.endingPage1546
prism.issueIdentifier9
prism.publicationDate2018
prism.publicationNameBioinformatics
prism.startingPage1538
prism.volume34
dc.identifier.doi10.17863/CAM.32012
dcterms.dateAccepted2017-12-14
rioxxterms.versionofrecord10.1093/bioinformatics/btx806
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-05
dc.contributor.orcidBender, Andreas [0000-0002-6683-7546]
dc.identifier.eissn1367-4811
rioxxterms.typeJournal Article/Review
cam.issuedOnline2017-12-15


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International