1. Identification of Drug–Target Interactions via Dual Laplacian Regularized Least Squares with Multiple Kernel Fusion.
- Author
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Ding, Yijie, Tang, Jijun, and Guo, Fei
- Subjects
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LEAST squares , *ALGORITHMS , *KERNEL operating systems , *INFORMATION resources , *BIPARTITE graphs , *MACHINE learning - Abstract
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. • DLapRLS employs alternating least squares algorithm to solve the final model. • Heterogeneous information (kernels) is integrated via multiple kernel learning. • For HSIC-MKL, we employ the Laplacian regular term to smooth the weights. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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