A heterogeneous label propagation approach to explore the potential associations between miRNA and disease

Abstract

Background: Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.

Methods: In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.

Results: HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.

Conclusions: All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.

Keywords: Disease; Label propagation; Multi-network; miRNA; miRNA-disease association.

Fig. 1

The disease DAG of esophageal neoplasms

Fig. 2

Flowchart of possible disease-miRNA association prediction based on the computational model of HLPMDA

Fig. 3

Predictive capability comparisons between HLPMDA and ten classical models of disease-miRNA association prediction (PBMDA, MCMDA, MaxFlow, HGIMDA, RLSMDA, HDMP, WBSMDA, MirAI, MIDP, and RWRMDA) in terms of ROC curve and AUC based on local and global LOOCV, respectively. As a result, HLPMDA achieved AUCs of 0.9232 and 0.8437 in the global and local LOOCV, significantly outperforming all the previous classical models