An Iterative GASVM-Based Method: Gene Selection and Classification of Microarray Data | SpringerLink
Skip to main content
Springer Nature Link
Log in

An Iterative GASVM-Based Method: Gene Selection and Classification of Microarray Data

  • Conference paper
Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living (IWANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5518))

Included in the following conference series:

Abstract

Microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). In this study, we propose an iterative method based on hybrid genetic algorithms to select a near-optimal (smaller) subset of informative genes in classification of the microarray data. The experimental results show that our proposed method is capable in selecting the near-optimal subset to obtain better classification accuracies than other related previous works as well as four methods experimented in this work. Additionally, a list of informative genes in the best gene subsets is also presented for biological usage.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Li, S., Wu, X., Hu, X.: Gene selection using genetic algorithm and support vectors machines. Soft Comput. 12, 693–698 (2008)

    Article  Google Scholar 

  2. Mohamad, M.S., Deris, S., Illias, R.M.: A hybrid of genetic algorithm and support vector machine for features selection and classification of gene expression microarray. J. Comput. Intell. Appl. 5, 1–17 (2005)

    Article  Google Scholar 

  3. Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)

    Article  Google Scholar 

  4. Mohamad, M.S., Omatu, S., Deris, S., Misman, M.F., Yoshioka, M.: A multi-objective strategy in genetic algorithm for gene selection of gene expression data. J. Artif. Life. & Rob. 13(2), 410–413 (2009)

    Article  Google Scholar 

  5. Peng, S., Xu, Q., Ling, X.B., Peng, X., Du, W., Chen, L.: Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Lett. 555, 358–362 (2003)

    Article  Google Scholar 

  6. Huang, H.L., Chang, F.L.: ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data. BioSystems 90, 516–528 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, 599-8531, Japan

    Mohd Saberi Mohamad, Sigeru Omatu & Michifumi Yoshioka

  2. Department of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310, Skudai, Johore, Malaysia

    Mohd Saberi Mohamad & Safaai Deris

Authors
  1. Mohd Saberi Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sigeru Omatu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Safaai Deris
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michifumi Yoshioka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan

    Sigeru Omatu

  2. Department of Informatics / CCTC, University of Minho, Braga, Portugal

    Miguel P. Rocha

  3. MAmI Research Lab, University of Castilla-La Mancha,, Ciudad Real, Spain

    José Bravo

  4. Department of Informatics, University of Vigo, Ourense, Spain

    Florentino Fernández

  5. Grupo de Investigación GICAP, Área de Lenguajes Higher Polytechnic School, Universidad de Burgos, Burgos, Spain

    Emilio Corchado

  6. Higher Polytechnic School, University of Burgos, Burgos, Spain

    Andrés Bustillo

  7. Department of Informatics, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mohamad, M.S., Omatu, S., Deris, S., Yoshioka, M. (2009). An Iterative GASVM-Based Method: Gene Selection and Classification of Microarray Data. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02481-8_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02480-1

  • Online ISBN: 978-3-642-02481-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation