Skip to main content
SpringerLink
Log in

Weighting and Feature Selection on Gene-Expression data by the use of Genetic Algorithms

  • Conference paper
  • First Online:
Artificial Neural Nets Problem Solving Methods (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2687))

Included in the following conference series:

Abstract

One of the most promising approaches for gaining insight into the biological activity of genes is to study their expression patterns in a variety of experimental conditions and contexts. In this work we present a genetic- algorithm-based approach for optimizing weighting schemes of variables used to improve clustering solutions. The same technique is used for feature selection and the detection of marker components in large datasets. An original string representation based on real numbers is used to encode the variable weight, and a modified silhouette value is used as fitness function. The strategy has a generic and parametric formulation, and effectiveness is demonstrated on gene-expression data.

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. Alizadeh, A.A.; et. al. (2000) “Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling” Nature 403, 503–511.

    Article  Google Scholar 

  2. Bittner, M.; et. al.(2000) “Molecular classification of cutaneous malignant melanoma by gene expression profiling”. Nature 406, 536–540.

    Article  Google Scholar 

  3. Brazma, A. and Vilo J. (2000), “Gene expression data analysis”, FEBS letters, vol 480, Issue 1, pp 17–24

    Article  Google Scholar 

  4. Davies, D.L. and Bouldin, D.W. (1979), “A cluster separation measure”, IEEE Trans. Patt.Anal. Mach. Intell. 1 pp. 224–227

    Article  Google Scholar 

  5. Dillon, W.R. and Goldstein, M. (1984) “Multivariate Analysis: Methods and Applications”. John Wiley & Sons, New York.

    Google Scholar 

  6. Eisen, M., Spellman, P.T., Botstein, D. and Brown, P.O. (1998) Proc. Natl. Acad. Sci. USA 95, 14863–14867

    Article  Google Scholar 

  7. Everitt, B. (1993), “Cluster analysis”, London: Edward Arnold, third edition.

    MATH  Google Scholar 

  8. Golberg, D.E., (1989), “Genetic Algorithms in Search, Optimisation and Machine Learning”, Addison Wesley Publishing Company.

    Google Scholar 

  9. Golub, T.R. et.al. (1999) “Molecular Classifications of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring”. Science 286:531–537

    Article  Google Scholar 

  10. Hartigan, J.A., (1975), “Clustering Algorithms”, Wiley, New-York

    MATH  Google Scholar 

  11. Iyer, V.R.; et.al (1999) “The transcriptional program in the response of human fibroblast to serum”. Science 283 (5398):83–87.

    Article  Google Scholar 

  12. Kaufman, L. and Rousseeuw, P.J. (1990). “Finding groups in data. An introduction to cluster analysis”. Wiley-Interscince, New York.

    MATH  Google Scholar 

  13. Jain, A,K, and Dubes, R.L. (1998), “Algorithms for clustering data”, Prentice-Hall

    Google Scholar 

  14. Lowell, D.R.; et al. (1997) “On the use of expected attainable discrimination for feature selection in large scale medical risk prediction problems”. CUED/F-INFENG/TR299

    Google Scholar 

  15. Perez, O. M.; Marin F. J.; and Trelles, O. (2001), “Improving Biological Sequence Property Distances by using a Genetic Algorithm”, IWANN 2001, LNCS 2085, pp. 539–546.

    Google Scholar 

  16. Rousseeuw, P.J. (1987) “Silhouettes: A graphical aid to the interpretations and validation of cluster analysis”. J. of Computational and Applied mathematics,20:53–65.

    Article  MATH  Google Scholar 

  17. Ríos Sixto (1983), “Análisis estadístico aplicado”. Madrid: Paraninfo,1983. 3a edición.

    Google Scholar 

  18. Sokal, R.R. (1977), “Clustering and classification: background and current directions”, In Van Ryzin, J. ed., Classification and Clustering, 1–15, Acad. Press.

    Google Scholar 

  19. Stefanini, F.M. and Camussi, A. (2000) “The reduction of large molecular profiles to informative components using a genetic algorithm” Bioinformatics 16, 923–931

    Article  Google Scholar 

  20. Tamayo, P.; et.al. (1999) “Interpreting patterns of gene expression with selforganizing maps: methods and application to hematopoietic differentiation”. Proc. Natl. Acad. Sci. USA 96 (6),2907–2912.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Architecture Department, University of Malaga, 29080, Malaga, Spain

    Olga M. Pérez, Manuel Hidalgo-Conde & Oswaldo Trelles

  2. Electronic Department, University of Malaga, 29080, Malaga, Spain

    Francisco J. Marín

  3. Integromics, Parque Científico de Madrid, 28049, Madrid, Spain

    Oswaldo Trelles

Authors
  1. Olga M. Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Hidalgo-Conde
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco J. Marín
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oswaldo Trelles
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pérez, O.M., Hidalgo-Conde, M., Marín, F.J., Trelles, O. (2003). Weighting and Feature Selection on Gene-Expression data by the use of Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_48

Download citation

  • DOI: https://doi.org/10.1007/3-540-44869-1_48

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40211-4

  • Online ISBN: 978-3-540-44869-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation