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Bioinformatics Advance Access published online on March 9, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm045
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Discovery of microRNA–mRNA modules via population-based probabilistic learning

Je-Gun Joung 1, Kyu-Baek Hwang 2, Jin-Wu Nam 1, Soo-Jin Kim 1 and Byoung -Tak Zhang 1,3,*

1Center for Bioinformation Technology, Seoul National University, Seoul 151-742, Korea, 2School of Computing, Soongsil University, Seoul 156-743, Korea, and 3School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea

*To whom correspondence should be addressed. Byoung -Tak Zhang, E-mail: btzhang{at}cse.snu.ac.kr


  Abstract

Motivation: MicroRNAs (miRNAs) and mRNAs constitute an important part of gen regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e., computationally predicted target genes of miRNAs and their respective expression profiles.

Results: We used data sets consisting of miRNA–target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA–mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA–mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA–mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.

Associate Editor: Prof. Satoru Miyano

Received on October 30, 2006; revised on December 15, 2006; accepted on February 4, 2006

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