Prediction of microRNA–disease associations with a Kronecker kernel matrix dimension reduction model

Abstract

Identifying the associations between human diseases and microRNAs is key to understanding pathogenicity mechanisms and important for uncovering novel prognostic markers. To date, a series of computational approaches have been developed for the prediction of disease–microRNA associations. However, these methods remain difficult to perform satisfactorily for diseases with a few known associated microRNAs. This study introduces a novel computational model, namely, the Kronecker kernel matrix dimension reduction (KMDR) model, for identifying potential microRNA–disease associations. This model combines microRNA space and disease space in a larger microRNA–disease space by using the Kronecker product or the Kronecker sum. The predictive performance of our proposed approach was evaluated and validated based on known association datasets. The experimental results show that KMDR achieves reliable prediction with an average AUC of 0.8320 for 22 complex diseases, which indeed outperforms other competitive methods. Moreover, case studies on kidney cancer, breast cancer, and esophageal cancer further demonstrate the applicability of our method in the identification of new disease–microRNA pairs. The source code of KMDR is freely available at https://github.com/ghli16/KMDR.