Protein phosphorylation is a kind of important post-translational modification of protein, which plays a critical role in many biological processes of eukaryote. Identifying kinase–substrate interactions is helpful to understand the mechanism of many diseases. Many computational algorithms for kinase–substrate interactions identification have been proposed. However, most of those methods are mainly focused on utilizing protein local sequence information. In this article, we propose a new computational method to predict kinase–substrate interactions based on protein–protein interaction (PPI) network. Different from existing methods, the PPI network is utilized to measure the similarities of kinase–kinase and substrate–substrate, respectively. Then, the pairwise similarities of kinase–kinase and substrate–substrate are adjusted based on the assumption that the similarities of kinase–kinase and substrate–substrate are more reliable if they are in the same cluster. Finally, the bi-random walk is used to predict potential kinase–substrate interactions. The experimental results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the case study demonstrates that it is effective in predicting potential kinase–substrate interactions.
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