Skip to main content
SpringerLink
Log in

Applying Memetic Algorithms to the Analysis of Microarray Data

  • Conference paper
  • First Online:
Applications of Evolutionary Computing (EvoWorkshops 2003)

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

Included in the following conference series:

Abstract

This work deals with the application of Memetic Algorithms to the Microarray Gene Ordering problem, a NP-hard problem with strong implications in Medicine and Biology. It consists in ordering a set of genes, grouping together the ones with similar behavior. We propose a MA, and evaluate the influence of several features, such as the intensity of local searches and the utilization of multiple populations, in the performance of the MA. We also analyze the impact of different objective functions on the general aspect of the solutions. The instances used for experimentation are extracted from the literature and represent real biological systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E. Alba. Parallel evolutionary algorithms can achieve super-linear performance. Information Processing Letters, 82(1):7–13, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  2. A.A. Alizadeh et al. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, 403:503–511, 2001.

    Article  Google Scholar 

  3. A. Arnone and B. Davidson. The hardwiring of development: Organization and function of genomic regulatory systems. Development, 124:1851–1864, 1997.

    Google Scholar 

  4. T. Bäck, D.B. Fogel, and Z. Michalewicz. Handbook of Evolutionary Computation. Oxford University Press, New York NY, 1997.

    MATH  Google Scholar 

  5. P.O. Brown and D. Botstein. Exploring the new world of the genome with DNA microarrays. Nature Genetics, 21:33–37, 1999.

    Article  Google Scholar 

  6. C. Cotta and P. Moscato. Inferring phylogenetic trees using evolutionary algorithms. In J.J. Merelo et al., editors, Parallel Problem Solving From Nature VII, volume 2439 of Lecture Notes in Computer Science, pages 720–729. Springer-Verlag, Berlin, 2002.

    Chapter  Google Scholar 

  7. J.L. DeRisi, V.R. Lyer, and P.O Brown. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278:680–686, 1997.

    Article  Google Scholar 

  8. 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 Sciences of the USA, 95:14863–14868, 1998.

    Google Scholar 

  9. D. Fasulo. An analysis of recent work on clustering algorithms. Technical Report UW-CSEO1-03-02, University of Washington, 1999.

    Google Scholar 

  10. P.M. França, A.S. Mendes, and P. Moscato. A memetic algorithm for the total tardiness single machine scheduling problem. European Journal of Operational Research, 132(1):224–242, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  11. V.R. Iyer et al. The transcriptional program in the response of human fibroblasts to serum. Science, 283:83–87, 1999.

    Article  Google Scholar 

  12. R.G. Jenner, M.M. Alba, C. Bosho., and P. Kellam. Kaposi’s sarcoma-associated herpesvirus latent and lytic gene expression as revealed by DNA arrays. Journal of Virology, 75:891–902, 2001.

    Article  Google Scholar 

  13. E.V. Koonin. The emerging paradigm and open problems in comparative genomics. Bioinformatics, 15:265–266, 1999.

    Article  Google Scholar 

  14. A.S. Mendes, P.M. França, and P. Moscato. NP-Opt: An optimization framework for NP problems. In Proceedings of POM2001-International Conference of the Production and Operations Management Society, pages 82–89, 2001.

    Google Scholar 

  15. P. Merz. Clustering gene expression profiles with memetic algorithms. In J.J. Merelo et al., editors, Parallel Problem Solving From Nature VII, volume 2439 of Lecture Notes in Computer Science, pages 811–820. Springer-Verlag, Berlin, 2002.

    Chapter  Google Scholar 

  16. P. Moscato and C. Cotta. A gentle introduction to memetic algorithms. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics. Kluwer Academic Publishers, Boston, 2002.

    Google Scholar 

  17. R. Tanese. Distributed genetic algorithms. In J.D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 434–439, San Mateo, CA, 1989. Morgan Kaufmann.

    Google Scholar 

  18. H.-K. Tsai, J.-M. Yang, and C.-Y. Kao. Applying genetic algorithms to finding the optimal gene order in displaying the microarray data. In W.B. Langdon et al., editors, Proceedings og the 2002 Genetic and Evolutionary Computation Conference. Morgan Kaufmann, 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Lenguajes y Ciencias de la Computación, University of Málaga, ETSI Informática, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  2. Faculdade de Engenharia Elétrica e de Computação, Universidade Estadual de Campinas, C.P. 6101, 13083-970, Campinas, Brazil

    Alexandre Mendes, Vinícius Garcia & Paulo França

  3. School of Electrical Engineering and Computer Science, University of Newcastle, 2308, Callaghan, NSW, Australia

    Pablo Moscato

Authors
  1. Carlos Cotta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandre Mendes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinícius Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paulo França
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pablo Moscato
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cotta, C., Mendes, A., Garcia, V., França, P., Moscato, P. (2003). Applying Memetic Algorithms to the Analysis of Microarray Data. 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_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-36605-9_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00976-4

  • Online ISBN: 978-3-540-36605-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation