Prediction of MicroRNA–disease Associations by Matrix Completion

Background: MicroRNAs play important roles in the progression of various diseases. Therefore, it is of vital importance to predict novel microRNA-disease associations for understanding disease mechanisms. Objective: As far as we see, there are generally three problems for the microRNA-disease association prediction. The first one is the lack of similarity among miRNAs. The second one is the presence of a few defined relationships between miRNAs and diseases. The insufficient number of available negative samples for studies on miRNA–disease associations is another troubling issue. We aimed to solve the three problems with the inductive matrix completion method. Method: In this paper, the inductive matrix completion method is exploited to overcome the three problems. We also contributed multiple feature sets to address problems related to insufficient miRNA–disease association data. The method could be applied to predict unknown microRNA-disease associations and new pathogenic miRNAs for well-characterized diseases. Results: Experiments can prove the performance of our inductive matrix completion method. The experiment is compared with several current methods through cross-validation. Our result reveals the superiority of our method to other approaches. Conclusion: We can conclude that the inductive matrix completion method is more suitable than transductive one, for the prediction of microRNA-disease associations.