Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks

Author:

Zhao Xian1,Chen Lei1,Guo Zi-Han1,Liu Tao1

Affiliation:

1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Abstract

Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informative drug features from several drug heterogeneous networks, representing different properties of drugs. For side effects, a network was also built, from where side effect features were extracted. These features can capture essential information about drugs and side effects in a network level. Drug and side effect features were combined together to represent each pair of drug and side effect, which was deemed as a sample in this study. Furthermore, they were fed into a random forest (RF) algorithm to construct the prediction model, called the RF network model. Results: The RF network model was evaluated by several tests. The average of Matthews correlation coefficients on the balanced and unbalanced datasets was 0.640 and 0.641, respectively. Conclusion: The RF network model was superior to the models incorporating other machine learning algorithms and one previous model. Finally, we also investigated the influence of two feature dimension parameters on the RF network model and found that our model was not very sensitive to these parameters.

Funder

Science and Technology Commission of Shanghai Municipality

Natural Science Foundation of Shanghai

Publisher

Bentham Science Publishers Ltd.

Subject

Computational Mathematics,Genetics,Molecular Biology,Biochemistry

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