Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

BMC Bioinformatics

Research Article

Lieven Verbeke¹  Dinh-Toi Chu²  Van-Huy Pham³  Le Hoang Son⁴  Duc-Hau Le⁵ 

[1] Department of Information Technology, Ghent University – imec, Ghent, Belgium;Faculty of Biology, Hanoi National University of Education, Hanoi, Vietnam;Institute of Research and Development, Duy Tan University, 03 Quang Trung, Da Nang, Vietnam;Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam;VNU University of Science, Vietnam National University, Hanoi, Vietnam;Vinmec Research Institute of Stem Cell and Gene Technology, 458 Minh Khai, Hai Ba Trung, Hanoi, Vietnam;

关键词: Disease-associated microRNAs;    Network analysis;    microRNA targets;    Random walk with restart;   

DOI  :  10.1186/s12859-017-1924-1

 received in 2017-03-13, accepted in 2017-11-06,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundMicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model.ResultsInstead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of “disease modules” in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations.ConclusionsSummarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of “disease modules” in these networks.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

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Fig. 5