Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Abstract

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.