Selecting informative genes from microarray data by using hybrid methods for cancer classification

被引：7

作者：

Mohamad M.S. ^{[1
]}

Omatu S. ^{[1
]}

Deris S. ^{[2
]}

Misman M.F. ^{[2
]}

Yoshioka M. ^{[1
]}

机构：

[1] Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai

[2] Department of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johore

来源：

Artificial Life and Robotics | 2009年 / 13卷 / 2期

关键词：

Cancer classification; Gene selection; Geneti calgorithm; Hybrid method; Microarray data;

D O I：

10.1007/s10015-008-0534-4

中图分类号：

学科分类号：

摘要：

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. © International Symposium on Artificial Life and Robotics (ISAROB). 2009.

引用

页码：414 / 417

页数：3

共 5 条

[1]

Wang L., Chu F., Xie W., Accurate cancer classification using expressions of very few genes, IEEE/ACM Trans Comput Bio Bioinf, 4, pp. 40-53, (2007)

[2]

Mohamad M.S., Omatu S., Deris S., Et al., A model for gene selection and classification of gene expression data, Artif Life Robotics, 11, pp. 219-222, (2007)

[3]

Mohamad M.S., Deris S., Illias R.M., A hybrid of genetic algorithm and support vector machine for features selection and classification of gene expression microarray, J Comput Intel Appl, 5, pp. 91-107, (2005)

[4]

Ambroise C., McLachlan G.J., Selection bias in gene extraction on the basis of microarray gene-expression data, Proc Natl Acad Sci USA, 99, 10, pp. 6562-6566, (2002)

[5]

Mohamad M.S., Omatu S., Deris S., Et al., A multi-objective strategy in genetic algorithm for gene selection of gene expression data, Artif Life Robotics, (2009)

← 1 →