Prediction of potential disease-associated microRNAs by composite network based inference

Abstract

MicroRNAs (miRNAs) act a significant role in multiple biological processes and their associations with the development of all kinds of complex diseases are much close. In the research area of biology, medicine, and bioinformatics, prediction of potential miRNA-disease associations (MDAs) on the base of a variety of heterogeneous biological datasets in a short time is an important subject. Therefore, we proposed the model of Composite Network based inference for MiRNA-Disease Association prediction (CNMDA) through applying random walk to a multi-level composite network constructed by heterogeneous dataset of disease, long noncoding RNA (lncRNA) and miRNA. The results showed that CNMDA achieved an AUC of 0.8547 in leave-one-out cross validation and an AUC of 0.8533+/-0.0009 in 5-fold cross validation. In addition, we employed CNMDA to infer novel miRNAs for kidney neoplasms, breast neoplasms and lung neoplasms on the base of HMDD v2.0. Also, we employed the approach for lung neoplasms on the base of HMDD v1.0 and for breast neoplasms that have no known related miRNAs. It was found that CNMDA could be seen as an applicable tool for potential MDAs prediction.

Figure 1

CNMDA got better AUCs of 0.8547 in the LOOCV in comparison of other four calculation approaches (RLSMDA, HDMP, WBSMDA, RKNNMDA).

Figure 2

Flowchart of CNMDA for potential MDAs prediction in the light of HMDD v2.0. Each node in the constructed multi-level composite network possesses original probability $p^0$. Final scores $p^{\infty }$ for MDAs would be gotten after employing RWR.