DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.
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Authors
Preuer, Kristina
Lewis, Richard PI
Hochreiter, Sepp
Bulusu, Krishna C
Klambauer, Günter
Publication Date
2018-05-01Journal Title
Bioinformatics
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Volume
34
Issue
9
Pages
1538-1546
Language
eng
Type
Article
Physical Medium
Print
Metadata
Show full item recordCitation
Preuer, K., Lewis, R. P., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2018). DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.. Bioinformatics, 34 (9), 1538-1546. https://doi.org/10.1093/bioinformatics/btx806
Abstract
Motivation: 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.
Keywords
Cell Line, Tumor, Humans, Neoplasms, Antineoplastic Combined Chemotherapy Protocols, Gene Expression Profiling, Computational Biology, Gene Expression Regulation, Neoplastic, Software, Support Vector Machine, Deep Learning
Identifiers
External DOI: https://doi.org/10.1093/bioinformatics/btx806
This record's URL: https://www.repository.cam.ac.uk/handle/1810/284638
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