Identification of drug–target interactions via fuzzy bipartite local model

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.