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Artificial Immune System for Classification of Cancer

  • Shin Ando
  • Hitoshi Iba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

This paper presents a method for cancer type classification based on microarray-monitored data. The method is based on artificial immune system(AIS), which utilizes immunological recognition for classification. The system evolutionarily selects important genes; optimize their weights to derive classification rules. This system was applied to gene expression data of acute leukemia patients to classify their cancer class. The primary result found few classification rules which correctly classified all the test samples and gave some interesting implications for feature selection principles.

Keywords

Support Vector Machine Training Sample Gene Expression Data Memory Cell Negative Selection 
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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References

  1. 1.
    A. Ben-Dor, N. Friedman, Z. Yakini, Class discovery in gene expression data, Proc. of the 5th Annual International Conference on Computational Molecular Biology, 31–38, 2001.Google Scholar
  2. 2.
    D. Dasgupta. Artificial Immune Systems and Their Applications. Springer, 1999.Google Scholar
  3. 3.
    D. Goldberg, B. Korb and K. Deb, Messy Genetic Algorithms: Motivation, Analysis and First Results, Complex Systems, 3:493–530, 1989zbMATHMathSciNetGoogle Scholar
  4. 4.
    Donna K. Slonim, Pablo Tamayo, Jill P. Mesirov, Todd R. Golub, Eric S. Lander, Class Prediction and Discovery Using Gene Expression Data, Proc. of the 4th Annual International Conference on Computational Molecular Biology(RECOMB), 263–272, 2000.Google Scholar
  5. 5.
    H. Liu, J. Li, L. Wong, A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns, in Proceeding of Genome Informatics Workshop, 2002Google Scholar
  6. 6.
    I. Guyon, J. Weston, S. Barnhill, V. Vapnik, Gene Selection for Cancer Classification using Support Vector Machines, Machine Learning Vol. 46 Issue 1–3, pp. 389–422, 2002zbMATHCrossRefGoogle Scholar
  7. 7.
    K.B. Hwang, D.Y. Cho, S.W. Wook Park, S.D. Kim, and B.Y. Zhang, Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis, in Proceedings of the First Conference on Critical Assessment of Microarray Data Analysis, CAMDA2000.Google Scholar
  8. 8.
    L. Li, C. R. Weinberg, T. A. Darden, L. G. Pedersen, Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method, Bioinformatics, Vol. 17, No. 12, pp. 1131–1142, 2001CrossRefGoogle Scholar
  9. 9.
    M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Science, 85:14863–14868, 1998.Google Scholar
  10. 10.
    P. Baldi and A. Long, A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes, Bioinformatics, 17:509–519, 2001.CrossRefGoogle Scholar
  11. 11.
    P.J. Park, M. Pagano, and M. Bonetti, A nonparametric scoring algorithm for identifying informative genes from microarry data, PSB2001, 6:52–63, 2001.Google Scholar
  12. 12.
    R. Kohavi and G. H. John, Wrappers for Feature Subset Selection, Artificial Intelligence, vol.97, 1–2, pp273–324, 1997zbMATHCrossRefGoogle Scholar
  13. 13.
    T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 2001Google Scholar
  14. 14.
    T.R. Golub, D.K. Slonim, P. Tamayo, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531–537, 1999.CrossRefGoogle Scholar
  15. 15.
    U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, and A. Levine. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon cancer tissues probed by oligonucleotide arrays. Cell Biology, 96:6745–6750, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Shin Ando
    • 1
  • Hitoshi Iba
    • 2
  1. 1.Dept. of Electronics EngineeringSchool of Engineering University of TokyoJapan
  2. 2.Dept. of Frontier Informatics, School of Frontier ScienceUniversity of TokyoJapan

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