Identification of drug–target interactions via fuzzy bipartite local model

Abstract

With the emergence of large-scale experimental data on genes and proteins, drug discovery and repositioning will be more difficult in the field of biomedical research. More and more resources are needed for detecting drug–target interactions (DTIs) in the experimental works. The interactions between drugs and targets could been seen as a bipartite network. Many computational methods have been developed to identify DTIs. However, most of them did not integrate multiple information and filter noise or outlier points. In this paper, we develop a fuzzy bipartite local model (FBLM) based on fuzzy least squares support vector machine and multiple kernel learning (MKL) for predicting DTIs. First, multiple kernels are constructed in drug and target spaces, respectively. Then, all corresponding kernels are combined by MKL algorithm in two spaces. Finally, FBLM is employed to identify DTIs. Our proposed approach is tested on four benchmark datasets under three types of cross validation. Comparing with existing outstanding methods, our method is a useful tool for the DTIs prediction.

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Acknowledgements

This work is supported by a grant from the National Science Foundation of China (NSFC 61772362 and 61902271), Natural Science Research Project of Jiangsu Higher Eduction Institutions of China (19KJB520014) and the Tianjin Research Program of Application Foundation and Advanced Technology (16JCQNJC00200). The authors also thank Dr. Yamanishi Y., Liu Y. and Nascimento A.C.A. for kindly providing datasets on their websites.

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Correspondence to Fei Guo.

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The source code, datasets and corresponding results are available at https://figshare.com/s/837b5e6631ed95dfc94e.

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Ding, Y., Tang, J. & Guo, F. Identification of drug–target interactions via fuzzy bipartite local model. Neural Comput & Applic 32, 10303–10319 (2020). https://doi.org/10.1007/s00521-019-04569-z

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Keywords

  • Drug–target interactions
  • Bipartite network
  • Multiple kernel learning
  • Fuzzy least squares support vector machines