Predicting Drug-target Interactions via FM-DNN Learning

(E-pub Ahead of Print)

Author(s): Jihong Wang, Hao Wang, Xiaodan Wang, Huiyou Chang*.

Journal Name: Current Bioinformatics

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Background: Identifying drug-target interactions (DTIs) is a major challenge for current drug discovery and drug repositioning. Compared to traditional experimental approaches, in silico methods are fast and inexpensive. With the increase in open-access experimental data, numerous computational methods have been applied to predict DTIs.

Methods: In this study,we propose an end-to-end learning model of factorization machine and deep neural network (FM-DNN), which emphasizes both low-order (first or second order) and high-order (higher than second order) feature interactions without any feature engineering other than raw features. This approach combines the power of FM and DNN learning for feature learning in a new neural network architecture.

Results: The experimental DTI basic features include drug characteristics (609), target characteristics (1819), plus drug ID, target ID,total 2430. We compare 8 models such as SVM, GBDT, WIDE-DEEP etc,the FM-DNN algorithm model obtains the best results of AUC(0.8866) and AUPR(0.8281).

Conclusions: Feature engineering is a job that requires expert knowledge,it is often difficult and time-consuming to achieve good results.FM-DNN can auto learn a lower-order expression by FM and a high-order expression by DNN.FM-DNN model has outstanding advantages over other commonly.

Keywords: drug-target interactions, prediction, factorization machines, DNN learning, machine learning, DrugBank

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(E-pub Ahead of Print)
DOI: 10.2174/1574893614666190227160538
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