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A Bayes Network Model to Determine MiRNA Gene Silence Mechanism

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Frontier and Future Development of Information Technology in Medicine and Education

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 269))

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Abstract

MicroRNAs (miRNAs) are small noncoding RNAs that silence gene expression by base pairing to mRNAs. They play important gene regulatory roles via hybridization to target mRNAs. The functional characterization of miRNAs relies heavily on the identification of their target mRNAs. Determining whether mRNA genes are regulated by translational repression or by post-transcriptional degradation is the premise of accurately identifying miRNA target genes. Combining expression profiling method with computational sequence analysis method, we present a Bayes network model to identify miRNA target genes as well as to determine the gene silence mechanism. The result shows that the learning algorithm of the model has detected 49 % candidate miRNA target genes at 5 % false detection rate and has determined 80 % genes regulated by post-transcriptional degradation mechanism at 3 % false detection rate. Our model precisely predicts miRNA gene silence mechanism and presents an efficient method to find out miRNA targets.

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References

  1. Stark A, Brennecke J, Russell RB et al (2003) Identification of drosophila MicroRNA targets. PLoS Biol 1:E60

    Article  Google Scholar 

  2. Enright AJ, John B, Gaul U et al (2003) MicroRNA targets in drosophila. Genome Biol 5:R1

    Article  Google Scholar 

  3. Lewis BP, Shih I, Jones-Rhoades MW et al (2003) Prediction of mammalian MicroRNA targets. Cell 115:787–798

    Article  Google Scholar 

  4. Vejnar CE, Zdobnov EM (2012) miRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res 40(22):11673–11683

    Article  Google Scholar 

  5. Lim LP et al (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433:769–773

    Article  Google Scholar 

  6. Huang JC, Morris QD, Frey BJ (2006) Detecting microRNA targets by linking sequence, microRNA and gene expression data. In: Proceedings of the tenth annual conference on research in computational molecular biology

    Google Scholar 

  7. Jordan MI, Ghahramani Z, Jaakkola TS et al (1999) An introduction to variational methods for graphical models. Learning in graphical models. MIT Press, Cambridge

    Google Scholar 

  8. Neal RM, Hinton GE (1998) A view of the EM algorithm that justifies incremental, sparse, and other variants, learning in graphical models. Kluwer Academic Publishers, Boston

    Google Scholar 

  9. Babak T, Zhang W, Morris Q, Blencowe BJ et al (2004) Probing microRNAs with microarrays: tissue specificity and functional inference. RNA 10:1813–1819

    Article  Google Scholar 

  10. Zhang W, Morris Q et al (2004) The functional landscape of mouse gene expression. J Biol 3:21–43

    Article  Google Scholar 

  11. Kislinger T et al (2006) Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125:173–186

    Article  Google Scholar 

  12. Zhang Y, Rajapakse JC (2009) Machine learning in bioinformatics. Wiley, New Jersey

    Google Scholar 

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Correspondence to Hao-yue Fu .

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© 2014 Springer Science+Business Media Dordrecht

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Fu, Hy., Lu, Xj., Zhang, Xd. (2014). A Bayes Network Model to Determine MiRNA Gene Silence Mechanism. In: Li, S., Jin, Q., Jiang, X., Park, J. (eds) Frontier and Future Development of Information Technology in Medicine and Education. Lecture Notes in Electrical Engineering, vol 269. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7618-0_43

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  • DOI: https://doi.org/10.1007/978-94-007-7618-0_43

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-7617-3

  • Online ISBN: 978-94-007-7618-0

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