CCRMDA: MiRNA-disease Association Prediction Based on Cascade Combination Recommendation Method on a Heterogeneous Network

Abstract

Background: MicroRNAs (miRNAs) are a class of short and endogenous single-stranded non-coding RNAs, with a length of 21-25nt. Many studies have proved that miRNAs are closely related to human diseases. Many algorithms based on network structure have been proposed to predict potential miRNA-disease associations. Methods: In this work, a cascade combination method based on network topology is developed to explore disease-related miRNAs. We name our method as CCRMDA. First, the hybrid recommendation algorithm is used for a rough recommendation, and then the structural perturbation method is used for a precise recommendation. A special perturbation set is constructed to predict new miRNA-disease associations in the miRNA-disease heterogeneous network. Results: To verify the effectiveness of CCRMDA, experimental analysis is performed on HMDD V2.0 and V3.2 datasets, respectively. For HMDD V2.0 dataset, CCRMDA is compared with several state-ofthe- art algorithms based on network structure, and the results show that CCRMDA has the best performance. The CCRMDA method also achieves excellent performance with an average AUC of 0.953 on HMDD V3.2 dataset. In addition, case studies further prove the effectiveness of CCRMDA. Conclusion: CCRMDA is a reliable method for predicting miRNA-disease.