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An iterative GASVM-based method: gene selection and classification of microarray data

Mohamad, Mohd. Saberi and Zainal, Anazida and Deris, Safa'ai (2009) An iterative GASVM-based method: gene selection and classification of microarray data. In: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing and Ambient Assisted Living. Springer, Berlin/ Heidelberg, pp. 187-194. ISBN 978-3-642-02481-8

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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.

Item Type:Book Section
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:14442
Deposited By: Siti Khairiyah Nordin
Deposited On:26 Aug 2011 03:38
Last Modified:09 Aug 2017 08:36

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