Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy.
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.
Liu
Bing
B
School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au
Li
Jiuyong
J
Tsykin
Anna
A
Liu
Lin
L
Gaur
Arti B
AB
Goodall
Gregory J
GJ
D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D004847:Epithelial Cells / Q000166:cytology; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks / Q000235:genetics*; D059630:Mesenchymal Stem Cells / Q000166:cytology; D035683:MicroRNAs / Q000378:metabolism*; D012333:RNA, Messenger / Q000378:metabolism*
D035683:MicroRNAs; D012333:RNA, Messenger