TY  - EJOU
AU  - CAO, BUWEN 
AU  - LUO, JIAWEI 
AU  - XIAO, SAINAN 
AU  - ZHAO, KAI 
AU  - YANG, SHULING 

TI  - INTS-MFS: A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity
T2  - BIOCELL

PY  - 2022
VL  - 46
IS  - 3
SN  - 1667-5746

AB  - 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.
KW  - Disease-related miRNA; MiRNA-disease association; Functional similarity; Network topological similarity

DO  - 10.32604/biocell.2022.017538