期刊
KNOWLEDGE-BASED SYSTEMS
卷 204, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.106254
关键词
Drug-Target Interactions; Bipartite network; Multiple Kernel Learning; Dual Laplacian Regularized Least Squares; Graph regularized model
资金
- National Natural Science Foundation of China [NSFC 61772362, 61902271, 61972280]
- Natural Science Research of Jiangsu Higher Education Institutions of China [19KJB520014]
Detection of Drug-Target Interactions (DTIs) is the time-consuming and laborious experiment via biochemical approaches. Machine learning based methods have been widely used to mine meaningful information of drug research. In this study, we establish a novel computational method to predict DTIs via Dual Laplacian Regularized Least Squares model (DLapRLS) with Hilbert-Schmidt Independence Criterion-based Multiple Kernel Learning (HSIC-MKL). Multiple kernels are built from different information sources (drug and target spaces). Then, above corresponding kernels are integrated by HSIC-MKL. At last, DLapRLS model is trained by Alternating Least Squares Algorithm (ALSA) and employed to predict new DTIs. On four benchmark datasets, the results of our method are comparable and even better than existing models. (C) 2020 Elsevier B.V. All rights reserved.
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