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Artificial Immune System for Classification of Cancer

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Applications of Evolutionary Computing (EvoWorkshops 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2611))

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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.

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References

  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. D. Dasgupta. Artificial Immune Systems and Their Applications. Springer, 1999.

    Google Scholar 

  3. D. Goldberg, B. Korb and K. Deb, Messy Genetic Algorithms: Motivation, Analysis and First Results, Complex Systems, 3:493–530, 1989

    MATH  MathSciNet  Google Scholar 

  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. 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, 2002

    Google Scholar 

  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, 2002

    Article  MATH  Google Scholar 

  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. 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, 2001

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. R. Kohavi and G. H. John, Wrappers for Feature Subset Selection, Artificial Intelligence, vol.97, 1–2, pp273–324, 1997

    Article  MATH  Google Scholar 

  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, 2001

    Google Scholar 

  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.

    Article  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Dept. of Electronics Engineering, School of Engineering University of Tokyo, Japan

    Shin Ando

  2. Dept. of Frontier Informatics, School of Frontier Science, University of Tokyo, Japan

    Hitoshi Iba

Authors
  1. Shin Ando
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  2. Hitoshi Iba
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Editor information

Editors and Affiliations

  1. Dept. of Computer Engineering, University of Parma, Parco Area delle Scienze 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  2. Computing Laboratory, University of Kent, Canterbury, Kent, CT2 7NF, UK

    Colin G. Johnson

  3. Dept. of Information and Communications Technologies Faculty of Computer Science, University of A Coruña, A Coruña, CP 15071, Spain

    Juan J. Romero Cardalda

  4. Dept. of Mathematics and Computer Science, Free University of Amsterdam, de Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands

    Elena Marchiori

  5. Schol of Systems Engineering, University of Reading, PO Box 225, Whiteknights, Reading, RG6 6AY, UK

    David W. Corne

  6. AnimatLab, 8 rue du capitaine Scott, 75015, Paris, France

    Jean-Arcady Meyer  & Agnès Guillot  & 

  7. SAP AG, Neurottstrasse 16, 69190, Walldorf, Germany

    Jens Gottlieb

  8. Parallel Computing and Complex Systems Group, University of Leipzig, Augustusplatz 10/11, 04109, Leipzig, Germany

    Martin Middendorf

  9. Algorithms and Data Structures Group Institute of Computer Graphics, Vienna University of Technology, Favoritenstrasse 9-11/186, 1040, Vienna, Austria

    Günther R. Raidl

  10. School of Computing, Napier University, 219 Colinton Road, Edinburgh, EH14 1DJ, UK

    Emma Hart

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© 2003 Springer-Verlag Berlin Heidelberg

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Ando, S., Iba, H. (2003). Artificial Immune System for Classification of Cancer. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_1

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  • DOI: https://doi.org/10.1007/3-540-36605-9_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00976-4

  • Online ISBN: 978-3-540-36605-8

  • eBook Packages: Springer Book Archive

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