Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/57593
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Type: Journal article
Title: Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy
Author: Liu, B.
Li, J.
Tsykin, A.
Liu, L.
Gaur, A.
Goodall, G.
Citation: BMC Bioinformatics, 2009; 10(408):1-19
Publisher: BioMed Central Ltd.
Issue Date: 2009
ISSN: 1471-2105
1471-2105
Statement of
Responsibility: 
Bing Liu, Jiuyong Li, Anna Tsykin, Lin Liu, Arti B. Gaur and Gregory J. Goodall
Abstract: Background: microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. Results: We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. Conclusions: This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.
Keywords: Epithelial Cells; Mesenchymal Stem Cells; MicroRNAs; RNA, Messenger; Bayes Theorem; Gene Expression Profiling; Computational Biology; Databases, Genetic; Gene Regulatory Networks
Description: © 2009 Liu et al; licensee BioMed Central Ltd.
RMID: 0020094518
DOI: 10.1186/1471-2105-10-408
Grant ID: http://purl.org/au-research/grants/arc/DP0559090
Appears in Collections:Medicine publications

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