A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network

被引:92
|
作者
Luo, Jiawei [1 ]
Xiao, Qiu [1 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Disease semantic similarity; Disease miRNA prediction; miRNA-disease association; miRNA similarity; Heterogeneous network; SEMANTIC SIMILARITY; GO TERMS; MIRNAS; GENES;
D O I
10.1016/j.jbi.2017.01.008
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
MicroRNAs (miRNAs) play a critical role by regulating their targets in post-transcriptional level. Identification of potential miRNA-disease associations will aid in deciphering the pathogenesis of human polygenic diseases. Several computational models have been developed to uncover novel miRNA-disease associations based on the predicted target genes. However, due to the insufficient number of experimentally validated miRNA-target interactions as well as the relatively high false-positive and false-negative rates of predicted target genes, it is still challenging for these prediction models to obtain remarkable performances. The purpose of this study is to prioritize miRNA candidates for diseases. We first construct a heterogeneous network, which consists of a disease similarity network, a miRNA functional similarity network and a known miRNA-disease association network. Then, an unbalanced bi-random walk based algorithm on the heterogeneous network (BRWH) is adopted to discover potential associations by exploiting bipartite subgraphs. Based on 5-fold cross validation, the proposed network-based method achieves AUC values ranging from 0.782 to 0.907 for the 22 human diseases and an average AUC of almost 0.846. The experiments indicated that BRWH can achieve better performances compared with several popular methods. In addition, case studies of some common diseases further demonstrated the superior performance of our proposed method on prioritizing disease-related miRNA candidates. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码:194 / 203
页数:10
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