ORIGINAL ARTICLE

Artificial Life and Robotics

, Volume 11, Issue 2, pp 219-222

First online:

A model for gene selection and classification of gene expression data

  • Mohd Saberi MohamadAffiliated withDepartment of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia Email author 
  • , Sigeru OmatuAffiliated withDepartment of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University
  • , Safaai DerisAffiliated withDepartment of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia
  • , Siti Zaiton Mohd HashimAffiliated withDepartment of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia

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Abstract

Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data which will maximize the classification accuracy. A model for gene selection and classification has been developed by using a filter approach, and an improved hybrid of the genetic algorithm and a support vector machine classifier. We show that the classification accuracy of the proposed model is useful for the cancer classification of one widely used gene expression benchmark data set.

Key words

Gene selection Hybrid approach Filter approach Gene expression data