Informative gene for cancer classification by using particle swarm optimization

M. S. Mohamad; S. Omatu; S. Deris; M. Yoshioka

Informative gene for cancer classification by using particle swarm optimization

M. S. Mohamad
, S. Omatu
, S. Deris
, M. Yoshioka

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Microarrays technology offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data that produced by the technology are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. Therefore, this article proposes particle swarm optimization (PSO) with the constraint of particle's velocities to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by two well-known gene expression data sets and obtained encouraging results as compared with the standard version of binary PSO.

Original language	English
Title of host publication	Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
Pages	650-653
Number of pages	4
Publication status	Published - 2010
Externally published	Yes
Event	15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan Duration: Feb 4 2010 → Feb 6 2010

Publication series

Name	Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10

Conference

Conference	15th International Symposium on Artificial Life and Robotics, AROB '10
Country/Territory	Japan
City	Beppu, Oita
Period	2/4/10 → 2/6/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Binary particle swarm optimization
Gene expression data
Gene selection
Optimization

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Human-Computer Interaction

Cite this

Informative gene for cancer classification by using particle swarm optimization. / Mohamad, M. S.; Omatu, S.; Deris, S. et al.
Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10. 2010. p. 650-653 (Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mohamad, MS, Omatu, S, Deris, S & Yoshioka, M 2010, Informative gene for cancer classification by using particle swarm optimization. in Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10. Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10, pp. 650-653, 15th International Symposium on Artificial Life and Robotics, AROB '10, Beppu, Oita, Japan, 2/4/10.

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title = "Informative gene for cancer classification by using particle swarm optimization",

abstract = "Microarrays technology offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data that produced by the technology are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. Therefore, this article proposes particle swarm optimization (PSO) with the constraint of particle's velocities to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by two well-known gene expression data sets and obtained encouraging results as compared with the standard version of binary PSO.",

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note = "15th International Symposium on Artificial Life and Robotics, AROB '10 ; Conference date: 04-02-2010 Through 06-02-2010",

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AU - Mohamad, M. S.

AU - Omatu, S.

AU - Deris, S.

AU - Yoshioka, M.

PY - 2010

Y1 - 2010

N2 - Microarrays technology offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data that produced by the technology are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. Therefore, this article proposes particle swarm optimization (PSO) with the constraint of particle's velocities to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by two well-known gene expression data sets and obtained encouraging results as compared with the standard version of binary PSO.

AB - Microarrays technology offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data that produced by the technology are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. Therefore, this article proposes particle swarm optimization (PSO) with the constraint of particle's velocities to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by two well-known gene expression data sets and obtained encouraging results as compared with the standard version of binary PSO.

KW - Binary particle swarm optimization

KW - Gene expression data

KW - Gene selection

KW - Optimization

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M3 - Conference contribution

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BT - Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10

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ER -

Informative gene for cancer classification by using particle swarm optimization

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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Cite this