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

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 regu...

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Bibliographic Details
Published in:Computers in biology and medicine 2012-04, Vol.42 (4), p.428-437
Main Authors: Zhang, Junpeng, Liu, Bing, He, Jianfeng, Ma, Lei, Li, Jiuyong
Format: Article
Language:English
Subjects:
Cell Line, Tumor
Cluster Analysis
Computational Biology - methods
Databases, Genetic
Epithelial-Mesenchymal Transition - genetics
Epithelial–mesenchymal transition
Functional regulatory modules
Gene Expression Regulation, Neoplastic
Humans
Internal Medicine
MicroRNAs - genetics
miRNA
Models, Genetic
mRNA
Other
Probabilistic topic model
Reproducibility of Results
RNA, Messenger - genetics
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Summary: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.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2011.12.011