Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification

Mohd Saberi Mohamad; Safaai Deris; Siti Zaiton Mohd Hashim

doi:10.1007/978-3-540-68017-8_133

Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification

Mohd Saberi Mohamad
, Safaai Deris
, Siti Zaiton Mohd Hashim

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

Abstract

The development of microarray-based highthroughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility.

Original language	English
Title of host publication	3rd Kuala Lumpur International Conference on Biomedical Engineering 2006
Editors	Fatimah Ibrahim, Noor Azuan Abu Osman, Juliana Usman, Nahrizul Adib Kadri
Publisher	Springer Verlag
Pages	528-532
Number of pages	5
ISBN (Electronic)	9783540680161
DOIs	https://doi.org/10.1007/978-3-540-68017-8_133
Publication status	Published - 2007
Externally published	Yes
Event	3rd Kuala Lumpur International Conference on Biomedical Engineering, Biomed 2006 - Kuala Lumpur, Malaysia Duration: Dec 11 2006 → Dec 14 2006

Publication series

Name	IFMBE Proceedings
Volume	15
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	3rd Kuala Lumpur International Conference on Biomedical Engineering, Biomed 2006
Country/Territory	Malaysia
City	Kuala Lumpur
Period	12/11/06 → 12/14/06

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Classification
Gene expression
Gene selection
Genetic algorithm
Microarray
Support vector machine

ASJC Scopus subject areas

Bioengineering
Biomedical Engineering

Access to Document

10.1007/978-3-540-68017-8_133

Cite this

Mohamad, M. S., Deris, S., & Hashim, S. Z. M. (2007). Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification. In F. Ibrahim, N. A. Abu Osman, J. Usman, & N. A. Kadri (Eds.), 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006 (pp. 528-532). (IFMBE Proceedings; Vol. 15). Springer Verlag. https://doi.org/10.1007/978-3-540-68017-8_133

Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification. / Mohamad, Mohd Saberi; Deris, Safaai; Hashim, Siti Zaiton Mohd.
3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. ed. / Fatimah Ibrahim; Noor Azuan Abu Osman; Juliana Usman; Nahrizul Adib Kadri. Springer Verlag, 2007. p. 528-532 (IFMBE Proceedings; Vol. 15).

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

Mohamad, MS, Deris, S & Hashim, SZM 2007, Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification. in F Ibrahim, NA Abu Osman, J Usman & NA Kadri (eds), 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. IFMBE Proceedings, vol. 15, Springer Verlag, pp. 528-532, 3rd Kuala Lumpur International Conference on Biomedical Engineering, Biomed 2006, Kuala Lumpur, Malaysia, 12/11/06. https://doi.org/10.1007/978-3-540-68017-8_133

Mohamad, Mohd Saberi ; Deris, Safaai ; Hashim, Siti Zaiton Mohd. / Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification. 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. editor / Fatimah Ibrahim ; Noor Azuan Abu Osman ; Juliana Usman ; Nahrizul Adib Kadri. Springer Verlag, 2007. pp. 528-532 (IFMBE Proceedings).

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title = "Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification",

abstract = "The development of microarray-based highthroughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility.",

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Y1 - 2007

N2 - The development of microarray-based highthroughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility.

AB - The development of microarray-based highthroughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying cancers. However, the large amount of data generated by microarrays requires effective selection of informative genes for cancer classification. Key issue that needs to be addressed is a selection of small number of informative genes that contribute to a disease from the thousands of genes measured on microarrays. This work deals with finding the small subset of informative genes from gene expression microarray data which maximize the classification accuracy. We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. We show that the classification accuracy of the proposed approach is superior to a number of current state-of-the-art methods of one widely used benchmark dataset. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biology and computer scientist researchers in order to explore the biological plausibility.

KW - Classification

KW - Gene expression

KW - Gene selection

KW - Genetic algorithm

KW - Microarray

KW - Support vector machine

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ER -

Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this