Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.

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, Johore, Malaysia

   Safaai Deris & Muhammad Faiz Misman

Corresponding authors

Correspondence to Mohd Saberi Mohamad or Sigeru Omatu.

This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008