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Zhang, Weijia; Le, Thuc Duy; Liu, Lin; Zhou, Zhi-Hua; Li, Jiuyong (2016)
Publisher: Public Library of Science
Journal: PLoS ONE
Languages: English
Types: Article
Subjects: Molecular Biology, Computational Biology, Research Article, Transfection, Graphs, Genetics, MicroRNAs, Gene Regulatory Networks, Molecular Biology Techniques, Data Visualization, Biology and life sciences, Non-coding RNA, RNA, Computer and Information Sciences, Research and Analysis Methods, Infographics, Post-Transcriptional Gene Regulation, Medicine, Nucleic acids, Q, R, Gene Regulation, Messenger RNA, Science, Biochemistry, Gene Expression
Motivation microRNAs (miRNAs) play crucial roles in post-transcriptional gene regulation of both plants and mammals, and dysfunctions of miRNAs are often associated with tumorigenesis and development through the effects on their target messenger RNAs (mRNAs). Identifying miRNA functions is critical for understanding cancer mechanisms and determining the efficacy of drugs. Computational methods analyzing high-throughput data offer great assistance in understanding the diverse and complex relationships between miRNAs and mRNAs. However, most of the existing methods do not fully utilise the available knowledge in biology to reduce the uncertainty in the modeling process. Therefore it is desirable to develop a method that can seamlessly integrate existing biological knowledge and high-throughput data into the process of discovering miRNA regulation mechanisms. Results In this article we present an integrative framework, CIDER (Causal miRNA target Discovery with Expression profile and Regulatory knowledge), to predict miRNA targets. CIDER is able to utilise a variety of gene regulation knowledge, including transcriptional and post-transcriptional knowledge, and to exploit gene expression data for the discovery of miRNA-mRNA regulatory relationships. The benefits of our framework is demonstrated by both simulation study and the analysis of the epithelial-to-mesenchymal transition (EMT) and the breast cancer (BRCA) datasets. Our results reveal that even a limited amount of either Transcription Factor (TF)-miRNA or miRNA-mRNA regulatory knowledge improves the performance of miRNA target prediction, and the combination of the two types of knowledge enhances the improvement further. Another useful property of the framework is that its performance increases monotonically with the increase of regulatory knowledge.

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