INTS-MFS: A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Identifying associations between microRNAs (miRNAs) and diseases is very important to understand the occurrence and development of human diseases. However, these existing methods suffer from the following limitation: first, some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources, i.e., disease similarity, protein interaction network, gene expression. Second, little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs. In this paper, we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity (INTS-MFS). The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics. INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies. As a results, 30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0. 29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC, PhenomiR2.0 and experimental reports. Moreover, INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer, which have not been reported. It provides biologists new clues for diagnosing breast and lung cancer.

Automated summary

This paper develops a novel model INTS-MFS that calculates miRNA similarity based on miRNA functional similarity and network topological characteristics. The model shows high accuracy in predicting disease-related miRNAs and discovers previously unknown associations between miRNAs and breast cancer and lung cancer, providing new clues for disease diagnosis.