Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods. (February 2019)

• Record Type: Journal Article
• Authors: Yan, Xiao-Ying; Zhang, Shao-Wu; He, Chang-Run
• Is Part Of: Computational biology and chemistry. Volume 78(2019)
• Page Start: 460
• Page End: 467
• Publication Date: 2019-02
• Subjects: Drug-target interaction -- Multiple kernel learning -- Clustering -- Bi-random walk; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.11.028
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods. Highlights: Integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning. Clustering methods are used for integrate network information about drugs and targets. Bi-random walk algorithm is adopted to infer the potential drug-target interactions. Achieves the best performance in terms of AUC and AUPR, and can effectively predict the potential drug-target interactions. Abstract: Background: Identification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction. Results: In this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions. Conclusions: Compared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.