A model for gene selection and classification of gene expression data

M. S. Mohamad; S. Omatu; S. Deris; S. Z.M. Hashim

A model for gene selection and classification of gene expression data

M. S. Mohamad
, S. Omatu
, S. Deris
, S. Z.M. Hashim

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

Abstract

Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data that will maximise the classification accuracy. A model for gene selection and classification has been developed by using a filter approach together with an improved hybrid of the genetic algorithm and a support vector machine classifier. It is shown that the classification accuracy of the proposed model is useful for the cancer classification of one that widely used gene expression benchmark data set.

Original language	English
Title of host publication	Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Pages	320-323
Number of pages	4
Publication status	Published - 2008
Externally published	Yes
Event	13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, Japan Duration: Jan 31 2008 → Feb 2 2008

Publication series

Name	Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

Conference

Conference	13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Country/Territory	Japan
City	Oita
Period	1/31/08 → 2/2/08

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Filter approach
Gene expression data
Gene selection
Hybrid approach

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Human-Computer Interaction

Cite this

A model for gene selection and classification of gene expression data. / Mohamad, M. S.; Omatu, S.; Deris, S. et al.
Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08. 2008. p. 320-323 (Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08).

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

Mohamad, MS, Omatu, S, Deris, S & Hashim, SZM 2008, A model for gene selection and classification of gene expression data. in Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08. Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08, pp. 320-323, 13th International Symposium on Artificial Life and Robotics, AROB 13th'08, Oita, Japan, 1/31/08.

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title = "A model for gene selection and classification of gene expression data",

abstract = "Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data that will maximise the classification accuracy. A model for gene selection and classification has been developed by using a filter approach together with an improved hybrid of the genetic algorithm and a support vector machine classifier. It is shown that the classification accuracy of the proposed model is useful for the cancer classification of one that widely used gene expression benchmark data set.",

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series = "Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08",

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booktitle = "Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08",

note = "13th International Symposium on Artificial Life and Robotics, AROB 13th'08 ; Conference date: 31-01-2008 Through 02-02-2008",

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AU - Deris, S.

AU - Hashim, S. Z.M.

PY - 2008

Y1 - 2008

N2 - Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data that will maximise the classification accuracy. A model for gene selection and classification has been developed by using a filter approach together with an improved hybrid of the genetic algorithm and a support vector machine classifier. It is shown that the classification accuracy of the proposed model is useful for the cancer classification of one that widely used gene expression benchmark data set.

AB - Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data that will maximise the classification accuracy. A model for gene selection and classification has been developed by using a filter approach together with an improved hybrid of the genetic algorithm and a support vector machine classifier. It is shown that the classification accuracy of the proposed model is useful for the cancer classification of one that widely used gene expression benchmark data set.

KW - Filter approach

KW - Gene expression data

KW - Gene selection

KW - Hybrid approach

UR - https://www.scopus.com/pages/publications/78449245485

UR - https://www.scopus.com/pages/publications/78449245485#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:78449245485

SN - 9784990288020

T3 - Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

SP - 320

EP - 323

BT - Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

T2 - 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

Y2 - 31 January 2008 through 2 February 2008

ER -

A model for gene selection and classification of gene expression data

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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Cite this