Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Abstract

MicroRNA (miRNA) molecules, which are effective on the initiation and progression of many different diseases, are a type of non-coding RNA with a length of about 22 nucleotides. Scientists have reported the importance of miRNAs in the prevention, diagnosis, and treatment of complex human diseases. Therefore, in the last decade, researchers have been working hard to find potential miRNA-disease associations. Many computational techniques have been developed because of the experimental techniques are time-consuming and expensive used to find new relationships between miRNAs and diseases. In this study, we suggested Kernelized Bayesian matrix factorization (KBMF) technique to predict new miRNA-disease relationships. We applied 5-fold cross validation technique and obtained an average value AUC of 0.9450. Also, we applied case studies based on breast, lung, and colon neoplasms to prove the performance of KBMF technique. The results showed that KBMF can be used as a reliable computational model to reveal possible miRNA-disease relationships.