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Mining Statistically Significant Target mRNA Association Rules Based on microRNA

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8032))

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Abstract

The relation of miRNA and mRNA are very important because miRNA can regulate almost all the biological process by cooperating with mRNA. However, the directed regulation among mRNA has not been concerned a lot. In this paper, we introduce association rule mining and hypothesis test to find the closely related mRNAs and their regulation direction based on their relation with miRNAs. Our research can further the understanding about miRNA and mRNA. Our results uncover the novel mRNA association patterns, which could not only help to construct the biological network, but also extend the application of association rule mining in bioinformatics.

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References

  1. Agrawal, R., Imieliński, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: SIGMOD, pp. 207–216 (1993)

    Google Scholar 

  2. Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning Journal 42, 31–60 (2001)

    Article  MATH  Google Scholar 

  3. Salleb-Aouissi, A., Vrain, C., Nortet, C.: QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules. In: IJCAI, pp. 1035–1040 (2007)

    Google Scholar 

  4. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB, pp. 487–499 (1994)

    Google Scholar 

  5. Horner, D.S., Pavesi, G., Castrignano, T., D’Onorio De Meo, P., Liuni, S., Sammeth, M., Picardi, E., Pesole, G.: Bioinformatics Approaches for Genomics and Post Genomics Applications of Next-Generation Sequencing. Briefings in Bioinformatics 11(2), 181–197 (2009)

    Article  Google Scholar 

  6. Dai, X., Zhuang, Z., Zhao, P.X.: Computational Analysis of miRNA Targets in Plants: Current Status and Challenges. Briefings in Bioinformatics, 1–7 (2010)

    Google Scholar 

  7. Watanabe, Y., Tomita, M., Kanai, A.: Computational Methods for microRNA Target Prediction. Methods in Enzymology 427, 65–86 (2007)

    Article  Google Scholar 

  8. Xu, J., Li, C., Li, Y., Lv, J., Ma, Y., Shao, T., Xu, L., Wang, Y., Du, L., Zhang, Y., Jiang, W., Li, C., Xiao, Y., Li, X.: MiRNA–miRNA Synergistic Network: Construction via Co-regulating Functional Modules and Disease miRNA Topological Features. Nucleic Acids Research, pp. 1–12 (2010)

    Google Scholar 

  9. Joung, J., Fei, Z.: Identification of microRNA Regulatory Modules in Arabidopsis via a Probabilistic Graphical Model. Bioinformatics 25(3), 387–393 (2009)

    Article  Google Scholar 

  10. Li, W., Zhang, S., Liu, C., Zhou, X.J.: Identifying Multi-layer Gene Regulatory Modules from Multi-Dimensional Genomic Data. Bioinformatics 28(29), 2458–2466 (2012)

    Article  Google Scholar 

  11. Bandyopadhyay, S., Mitra, R., Maulik, U., Zhang, M.Q.: Development of the Human Cancer MicroRNA Network. Silence 1, 6 (2010)

    Article  Google Scholar 

  12. Tran, D.H., Satou, K., Ho, T.B.: Finding MicroRNA Regulatory Modules in Human Genome Using Rule Induction. BMC Bioinformatics 9, S5 (2008)

    Article  Google Scholar 

  13. Bowman, A., Azzalini, A.: Applied Smoothing Techniques for Data Analysis. Oxford University Press, London (1997)

    MATH  Google Scholar 

  14. Li, X., Shen, Y., Ichikawa, H., Antes, T., Goldberg, G.S.: Regulation of miRNA expression by Src and contact normalization: effects on nonanchored cell growth and migration. Oncogene, 1–12 (2009)

    Google Scholar 

  15. Zhang, Z., Yu, J., Li, D., Zhang, Z., Liu, F., Zhou, X., Wang, T., Ling, Y., Su, Z.: PMRD: Plant MicroRNA Database. Nucleic Acids Research 38, D806–D813 (2010)

    Article  Google Scholar 

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Chen, F., Wang, T., Wang, Y., Li, S., Wang, J. (2013). Mining Statistically Significant Target mRNA Association Rules Based on microRNA. In: Qin, Z., Huynh, VN. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2013. Lecture Notes in Computer Science(), vol 8032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39515-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-39515-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39514-7

  • Online ISBN: 978-3-642-39515-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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