Skip to main content

Advertisement

SpringerLink
Log in

Gene subset selection using an iterative approach based on genetic algorithms

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other previous related work, as well as to four methods tried in this work. In addition, 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 article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Mohamad MS, Deris S, Illias RM (2005) 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Mohamad MS, Omatu S, Deris S, et al (2009) A multi-objective strategy in genetic algorithm for gene selection of gene expression data. Artif Life Robotics 13:410–413

    Article  Google Scholar 

  5. Peng S, Xu Q, Ling XB, et al (2003) Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Lett 555:358–362

    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

Corresponding authors

Correspondence to Mohd Saberi Mohamad or Sigeru Omatu.

Additional information

This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009

About this article

Cite this article

Mohamad, M.S., Omatu, S., Deris, S. et al. Gene subset selection using an iterative approach based on genetic algorithms. Artif Life Robotics 14, 12–15 (2009). https://doi.org/10.1007/s10015-009-0711-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-009-0711-0

Key words

Search

Navigation