Informative MicroRNA Expression Patterns for Cancer Classification

  • Yun Zheng
  • Chee Keong Kwoh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3916)


Some non-coding small RNAs, known as microRNAs (miRNAs), have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNA genes often results in the development of cancer. In this paper, we find discriminatory miRNA patterns for cancer classification from miRNA expression profiles. The experimental results show that the expression patterns from a small set of miRNAs are very accurate in prediction. Further, the experimental results also suggest that the expression patterns of these informative miRNAs are conserved in different vertebrates during the evolution process.


Mutual Information miRNA Expression miRNA Gene Probabilistic Neural Network Class Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Ambros, V.: The functions of animal microRNAs. Nature 431, 350–355 (2004)CrossRefGoogle Scholar
  2. 2.
    Bartel, D.P.: MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell. 116, 281–297 (2004)CrossRefGoogle Scholar
  3. 3.
    Alvarez-Garcia, I., Miska, E.A.: MicroRNA functions in animal development and human disease. Development 132, 4653–4662 (2005)CrossRefGoogle Scholar
  4. 4.
    Gregory, R.I., Shiekhattar, R.: MicroRNA Biogenesis and Cancer. Cancer Res. 65(9), 3509–3512 (2005)CrossRefGoogle Scholar
  5. 5.
    He, L., Thomson, J., Hemann, M., Hernando-Monge, E., Mu, D., Goodson, S., Powers, S., Cordon-Cardo, C., Lowe, S., Hannon, G., Hammond, S.: A MicroRNA polycistron as a potential human oncogene. Nature 435, 828–833 (2005)CrossRefGoogle Scholar
  6. 6.
    Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B.L., Mak, R.H., Ferrando, A.A., Downing, J.R., Jacks, T., Horvitz, H.R., Golub, T.R.: MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005)CrossRefGoogle Scholar
  7. 7.
    Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  8. 8.
    Specht, D.F.: Probabilistic neural networks. Neural Networks 3, 109–118 (1990)CrossRefGoogle Scholar
  9. 9.
    Zheng, Y., Kwoh, C.K.: Identifying simple discriminatory gene vectors with an information theory approach. In: Proceedings of the 4th Computational Systems Bioinformatics Conference, CSB 2005, pp. 12–23. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  10. 10.
    Langley, P., Iba, W., Thompson, K.: An analysis of bayesian classifiers. In: National Conference on Artificial Intelligence, pp. 223–228 (1992)Google Scholar
  11. 11.
    Shannon, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1963)MATHGoogle Scholar
  12. 12.
    McEliece, R.: The Theory of Information and Coding: A Mathematical Framework for Communication. Encyclopedia of Mathematics and Its Applications, vol. 3. Addison-Wesley Publishing Company, Reading (1977)MATHGoogle Scholar
  13. 13.
    Cover, T., Thomas, J.: Elements of Information Theory. John Wiley & Sons, Inc., Chichester (1991)CrossRefMATHGoogle Scholar
  14. 14.
    Cherkassky, V., Mulier, F.: Learning from Data: Concepts, Theory, and Methods. John Wiley & Sons, Inc., New York (1998)MATHGoogle Scholar
  15. 15.
    Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, IJCAI 1993, Chambery, France, pp. 1022–1027 (1993)Google Scholar
  16. 16.
    Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, I.: Data mining in bioinformatics using Weka. Bioinformatics 20(15), 2479–2481 (2004)CrossRefGoogle Scholar
  17. 17.
    Quinlan, J.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  18. 18.
    Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods: support vector learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  19. 19.
    Cohen, W.W.: Fast effective rule induction. In: Proc. 12th International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann, San Francisco (1995)Google Scholar
  20. 20.
    Meltzer, P.S.: Cancer genomics: Small RNAs with big impacts. Nature 435, 745–746 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yun Zheng
    • 1
  • Chee Keong Kwoh
    • 1
  1. 1.Bioinformatics Research Center, School of Computer EngineeringNanyang Technology UniveritySingapore

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