Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

Liu, H.; Deng, L.; Cai, Y.; Zhang, W.; Yang, W.; Gao, B.

doi:10.1101/2020.02.06.930503

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bioRxiv · 10.1101/2020.02.06.930503

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

Liu, H.·Deng, L.·Cai, Y.·Zhang, W.·Yang, W.·Gao, B.

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Abstract

MotivationTo efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in-silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of mechanism of drug action or limited performance in modeling drug sensitivity. ResultsIn this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To make advantage of both the excellent predictive ability of deep neural network and the biological knowledge of pathways, we reshape the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets, and demonstrate that pathDNN significantly outperformed canonical DNN model and seven other classical regression models. Most importantly, we observed remarkable activity decreases of disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments show that pathDNN achieves pharmacological interpretability and predictive ability in modeling drug sensitivity to cancer cells. AvailabilityThe web server, as well as the processed data sets and source codes for reproducing our work, is available at http://pathdnn.denglab.org

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Liu, H., Deng, L., Cai, Y., Zhang, W., Yang, W., Gao, B.. 2020-02-07. Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity. https://doi.org/10.1101/2020.02.06.930503

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