TY - GEN
T1 - Multi-objective optimization using genetic algorithm for gene selection from microarray data
AU - Mohamad, Mohd Saberi
AU - Omatu, Sigeru
AU - Deris, Safaai
AU - Yoshioka, Michifumi
PY - 2008
Y1 - 2008
N2 - Microarray technology has been increasingly used in cancer research because of its potential for measuring expression levels of thousands of genes simultaneously in tissue samples. It is used to collect the information from tissue samples regarding gene expression differences that could be useful for cancer classification. However, this classification task faces many challenges due to availability of a smaller number of samples compared to the huge number of genes, and many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to an improved accuracy of the classification. Hence, this paper proposes a solution to the problem of gene selection by using a multi-objective approach in genetic algorithm. This approach is experimented on two microarray data sets such as Lung cancer and Mixed-Lineage Leukemia cancer. It obtains encouraging result on those data sets as compared with an approach that uses singleobjective approach.
AB - Microarray technology has been increasingly used in cancer research because of its potential for measuring expression levels of thousands of genes simultaneously in tissue samples. It is used to collect the information from tissue samples regarding gene expression differences that could be useful for cancer classification. However, this classification task faces many challenges due to availability of a smaller number of samples compared to the huge number of genes, and many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to an improved accuracy of the classification. Hence, this paper proposes a solution to the problem of gene selection by using a multi-objective approach in genetic algorithm. This approach is experimented on two microarray data sets such as Lung cancer and Mixed-Lineage Leukemia cancer. It obtains encouraging result on those data sets as compared with an approach that uses singleobjective approach.
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U2 - 10.1109/ICCCE.2008.4580821
DO - 10.1109/ICCCE.2008.4580821
M3 - Conference contribution
AN - SCOPUS:51849100355
SN - 9781424416929
T3 - Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development
SP - 1331
EP - 1334
BT - Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08
T2 - International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development
Y2 - 13 May 2008 through 15 May 2008
ER -
