Inferring functional miRNA-mRNA regulatory modules in epithelial-mesenchymal transition with a probabilistic topic model

Authors:
Junpeng Zhang;Bing Liu;Jianfeng He;Lei Ma;Jiuyong Li
Affiliations:
Kunming University of Science and Technology, Kunming, China;School of Biomedical Sciences and Pharmacy, The University of Newcastle, NSW, Australia;Kunming University of Science and Technology, Kunming, China;Kunming University of Science and Technology, Kunming, China;School of Computer and Information Science, University of South Australia, Australia
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

MicroRNAs (miRNAs) play important roles in gene regulatory networks. In this paper, we propose a probabilistic topic model to infer regulatory networks of miRNAs and their target mRNAs for specific biological conditions at the post-transcriptional level, so-called functional miRNA-mRNA regulatory modules (FMRMs). The probabilistic model used in this paper can effectively capture the relationship between miRNAs and mRNAs in specific cellular conditions. Furthermore, the proposed method identifies negatively and positively correlated miRNA-mRNA pairs which are associated with epithelial, mesenchymal, and other condition in EMT (epithelial-mesenchymal transition) data set, respectively. Results on EMT data sets show that the inferred FMRMs can potentially construct the biological chain of 'miRNA-mRNA-condition' at the post-transcriptional level.