Evolving artificial neural networks for DNA microarray analysis

Kyung Joong Kim, Sung Bae Cho

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

DNA microarray technology provides a format for the simultaneous measurement of the expression level of thousands of genes in a single hybridization assay. One exciting result of microarray technology has been the demonstration that patterns of gene expression can distinguish between tumors of different anatomical origins. Standard statistical methodologies in classification and prediction do not work well or even at all when N (the number of samples) < p (genes). Modification of existing statistical methodologies or development of new methodologies are needed for the analysis of cancer. Recently, designing artificial neural networks (ANNs) by evolutionary algorithms has emerged as a preferred alternative to the common practice of selecting the apparent best network. We propose an evolutionary neural network that classifies gene expression profiles into normal or colon cancer cell. Colon cancer is the second only to lung cancer as a cause of cancer-related mortality in Western countries. Colon cancer is a genetic disease, propagated by the acquisition of somatic alterations that influence gene expression. Experimental results on colon microarray data with evolutionary neural network show that the proposed method can perform better than other classifiers. Contribution of this article is applying evolutionary neural network to gene expression classification problem.

Original languageEnglish
Pages2370-2377
Number of pages8
DOIs
Publication statusPublished - 2003 Jan 1
Event2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia
Duration: 2003 Dec 82003 Dec 12

Other

Other2003 Congress on Evolutionary Computation, CEC 2003
CountryAustralia
CityCanberra, ACT
Period03/12/803/12/12

Fingerprint

Microarray Analysis
DNA Microarray
Microarrays
Gene expression
Artificial Neural Network
Evolutionary Neural Networks
Cancer
DNA
Neural networks
Gene Expression
Genes
Methodology
Gene
Gene Expression Profile
Evolutionary algorithms
Lung Cancer
Tumors
Assays
Microarray Data
Classifiers

All Science Journal Classification (ASJC) codes

  • Computational Mathematics

Cite this

Kim, K. J., & Cho, S. B. (2003). Evolving artificial neural networks for DNA microarray analysis. 2370-2377. Paper presented at 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia. https://doi.org/10.1109/CEC.2003.1299384
Kim, Kyung Joong ; Cho, Sung Bae. / Evolving artificial neural networks for DNA microarray analysis. Paper presented at 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia.8 p.
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Kim, KJ & Cho, SB 2003, 'Evolving artificial neural networks for DNA microarray analysis', Paper presented at 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia, 03/12/8 - 03/12/12 pp. 2370-2377. https://doi.org/10.1109/CEC.2003.1299384

Evolving artificial neural networks for DNA microarray analysis. / Kim, Kyung Joong; Cho, Sung Bae.

2003. 2370-2377 Paper presented at 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia.

Research output: Contribution to conferencePaper

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Kim KJ, Cho SB. Evolving artificial neural networks for DNA microarray analysis. 2003. Paper presented at 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, Australia. https://doi.org/10.1109/CEC.2003.1299384