Mohamad, Mohd. Saberi and Omatu, Sigeru and Deris, Safaai and Yoshioka, M. (2009) Gene subset selection using an iterative approach based on genetic algorithms. In: Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09. ISAROB, pp. 758-761. ISBN 978-499028803-7
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Microarray data are expected to be useful for cancer classification. The main problem that needs to be addressed 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 a small number of samples compared to a huge number of genes (higher-dimensional data). Hence, this paper aims to select a near-optimal (smaller) subset of informative genes that is most relevant for the cancer classification. To achieve the 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 related previous works as well as four methods experimented in this work. In addition a list of informative genes in the best gene subsets is also presented for biological usage.
|Item Type:||Book Section|
|Uncontrolled Keywords:||gene selection, genetic algorithm, iterative approach, microarray data|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Ms Zalinda Shuratman|
|Deposited On:||30 Sep 2011 15:19|
|Last Modified:||30 Sep 2011 15:19|
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