DeepSynergy: Predicting anti-cancer drug synergy with Deep Learning
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 normalisation strategy to account for input data heterogeneity, and conical layers for modelling. DeepSynergy significantly
outperformed other machine learning methods like Gradient Boosted Trees, Random Forests, Support Vector Machines, and Elastic Nets on this
large publicly available synergy data set based on the MSE, which is an improvement by 7.2% over the second best method. The mean Pearson
correlation coefficient between the measured and the predicted values of DeepSynergy is 0.73. Applying DeepSynergy for classification resulted
in an AUC of 0.90.
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