Mohamad, M. S. and Omatu, S. and Deris, S. and Yoshioka, M. (2009) Gene subset selection using an iterative approach based on genetic algorithms. In: The Fourteenth International Symposium on Artificial Life and Robotics 2009 (AROB 14th 09), 2009, B-Con Plaza, Beppu, Oita Japan.
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Official URL: http://dx.doi.org/10.1007/s10015-009-0711-0
Microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this 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 previous related work, as well as to four methods tried in this work. In addition, a list of informative genes in the best gene subsets is also presented for biological usage.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Liza Porijo|
|Deposited On:||22 Sep 2011 09:51|
|Last Modified:||22 Sep 2011 09:51|
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