Decision Tree Classifier for Classification of Plant and Animal Micro RNA’s

  • Bhasker Pant
  • Kumud Pant
  • K. R. Pardasani
Part of the Communications in Computer and Information Science book series (CCIS, volume 51)


Gene expression is regulated by miRNAs or micro RNAs which can be 21-23 nucleotide in length. They are non coding RNAs which control gene expression either by translation repression or mRNA degradation. Plants and animals both contain miRNAs which have been classified by wet lab techniques. These techniques are highly expensive, labour intensive and time consuming. Hence faster and economical computational approaches are needed. In view of above a machine learning model has been developed for classification of plant and animal miRNAs using decision tree classifier. The model has been tested on available data and it gives results with 91% accuracy.


Micro RNA’s Decision Tree Classification Cross validation 


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  1. 1.
    Anthony, A.M., Peter, M.W.: Plant and animal microRNAs: similarities and differences. SpringerLink Funct Integr Genomics 5, 129–135 (2005)CrossRefGoogle Scholar
  2. 2.
    Pierre, B., Soren, B.: Bioinformatics the Machine Learning Approach, 2nd edn. (2001)Google Scholar
  3. 3.
    Aagaard, L., Rossi, J.J.: RNAi Therapeutics: Principles, Prospects and Challenges. Elsevier Science, Amsterdam (2007)Google Scholar
  4. 4.
    McDaneld, T.G., Wiedmann, R.T., Miles, J.R., Cushman, R., Vallet, R., Smith, T.P.L.: NE microRNA technology in livestock: expression profiling of bovine oocyte and developmental stages of porcine skeletal muscle. USDA/ARS U.S (2007)Google Scholar
  5. 5.
    Witten, I.H., Frank, E.: Data Mining – Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco (2005)Google Scholar
  6. 6.
    De, F.L.: Mining housekeeping genes with a Naive Bayes classifier. BMC Genomics 7, 277 (2006)CrossRefGoogle Scholar
  7. 7.
    Weka Data Mining Java Software,
  8. 8.
    Jones-Rhoades, M.W., Bartel, D.P.: Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol. Cell 14, 787–799 (2004)CrossRefGoogle Scholar
  9. 9.
    Meyerowitz, E.M.: Plants compared to animals: the broadest comparative study of development. Science 295, 1482–1485 (2002)CrossRefGoogle Scholar
  10. 10.
    Floyd, S.F., Bowman, J.L.: Ancient microRNA target sequences in plants. Nature 428, 485–486 (2004)CrossRefGoogle Scholar
  11. 11.
    Ambros, V.: The functions of animal microRNAs. Nature 431, 244–350 (2004)CrossRefGoogle Scholar
  12. 12.
    Langley, P., Sage, S.: Elements of machine learning. Morgan Kaufmann, San Francisco (1994)Google Scholar
  13. 13.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar
  14. 14.
    Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian network: The combination of knowledge and statistical data. Machine Learning 20(3), 197–243 (1995)MATHGoogle Scholar
  15. 15.
    Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)MATHCrossRefGoogle Scholar
  16. 16.
    Borenstein, E., Eytan, R.: Direct evolution of genetic robustness in microRNA. PNAS 103, 6593–6598 (2006)CrossRefGoogle Scholar
  17. 17.
    Micro RNARegistry,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bhasker Pant
    • 1
  • Kumud Pant
    • 1
  • K. R. Pardasani
    • 2
  1. 1.Department of BioinformaticsMANITBhopalIndia
  2. 2.Department of MathematicsMANITBhopalIndia

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