1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054 , China 2. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China 3. Beidahuang Industry Group General Hospital, Harbin 150088, China 4. School of Informatics, Xiamen University, Xiamen 361005, China 5. Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou 646000, China 6. Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Numerous studies have demonstrated that human microRNAs (miRNAs) and diseases are associated and studies on the microRNA-disease association (MDA) have been conducted. We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning (HSIC-MKL) to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases, and improve the model effect. We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL. Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs. The results of the experiment show that the approach we proposed has a good effect, and, in some respects, exceeds what existing models can do.
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