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

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