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

Authors:
Mohd Saberi Mohamad;Sigeru Omatu;Safaai Deris;Michifumi Yoshioka
Affiliations:
Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan 599-8531 and Department of Software Engineering, Faculty of ...;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan 599-8531;Department of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Malaysia 81310;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan 599-8531
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

Citing 2
Cited 0

Gene selection using genetic algorithm and support vectors machines

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
A review of feature selection techniques in bioinformatics

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

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.