Brief Communication: Identifying the target mRNAs of microRNAs in colorectal cancer
Computational Biology and Chemistry
Discovery of functional miRNA-mRNA regulatory modules with computational methods
Journal of Biomedical Informatics
Exploring the ncRNA-ncRNA patterns based on bridging rules
Journal of Biomedical Informatics
Computers in Biology and Medicine
Computational regulatory network construction from microRNA and transcription factor perspectives
ACM SIGBioinformatics Record
Challenges in the miRNA research
International Journal of Bioinformatics Research and Applications
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Motivation: MicroRNAs (miRNAs) and mRNAs constitute an important part of gene 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. Contact: btzhang@bi.snu.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.