A recursive genetic algorithm to automatically select genes for cancer classification

Mohd Saberi Mohamad; Sigeru Omatu; Safaai Deris; Michifumi Yoshioka

doi:10.1007/978-3-540-85861-4_20

A recursive genetic algorithm to automatically select genes for cancer classification

Mohd Saberi Mohamad
, Sigeru Omatu
, Safaai Deris
, Michifumi Yoshioka

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

1 Citation (Scopus)

Abstract

Gene expression technology namely microarray, offers the ability to measure the expression levels of thousands of genes simultaneously in a biological organism. Microarray data are expected to be of significant help in the development of efficient cancer diagnosis and classification platform. The main problem that needs to be addressed is the selection of a small subset of genes that contributes to a disease from the thousands of genes measured on microarray that are inherently noisy. Most approaches from previous works have selected the numbers of genes manually and thus, have caused difficulty, especially for beginner biologists. Hence, this paper aims to automatically select a small subset of informative genes that is most relevant for the cancer classification. In order to achieve this aim, a recursive genetic algorithm has been proposed. Experimental results show that the gene subset is small in size and yield better classification accuracy as compared with other previous works as well as four methods experimented in this work. A list of informative genes in the best subsets is also presented for biological usage.

Original language	English
Title of host publication	2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)
Editors	Juan Corchado, Juan De Paz, Miguel Rocha, Florentino Fernandez Riverola
Pages	166-174
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-540-85861-4_20
Publication status	Published - 2009
Externally published	Yes

Publication series

Name	Advances in Soft Computing
Volume	49
ISSN (Print)	1615-3871
ISSN (Electronic)	1860-0794

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Cancer classification
Gene selection
Genetic Algorithm
Microarray data
Recursive genetic algorithm
Support vector machine

ASJC Scopus subject areas

Computer Science (miscellaneous)
Computational Mechanics
Computer Science Applications

Access to Document

10.1007/978-3-540-85861-4_20

Cite this

Mohamad, M. S., Omatu, S., Deris, S., & Yoshioka, M. (2009). A recursive genetic algorithm to automatically select genes for cancer classification. In J. Corchado, J. De Paz, M. Rocha, & F. F. Riverola (Eds.), 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008) (pp. 166-174). (Advances in Soft Computing; Vol. 49). https://doi.org/10.1007/978-3-540-85861-4_20

A recursive genetic algorithm to automatically select genes for cancer classification. / Mohamad, Mohd Saberi; Omatu, Sigeru; Deris, Safaai et al.
2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). ed. / Juan Corchado; Juan De Paz; Miguel Rocha; Florentino Fernandez Riverola. 2009. p. 166-174 (Advances in Soft Computing; Vol. 49).

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

Mohamad, MS, Omatu, S, Deris, S & Yoshioka, M 2009, A recursive genetic algorithm to automatically select genes for cancer classification. in J Corchado, J De Paz, M Rocha & FF Riverola (eds), 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol. 49, pp. 166-174. https://doi.org/10.1007/978-3-540-85861-4_20

Mohamad MS, Omatu S, Deris S, Yoshioka M. A recursive genetic algorithm to automatically select genes for cancer classification. In Corchado J, De Paz J, Rocha M, Riverola FF, editors, 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). 2009. p. 166-174. (Advances in Soft Computing). doi: 10.1007/978-3-540-85861-4_20

Mohamad, Mohd Saberi ; Omatu, Sigeru ; Deris, Safaai et al. / A recursive genetic algorithm to automatically select genes for cancer classification. 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). editor / Juan Corchado ; Juan De Paz ; Miguel Rocha ; Florentino Fernandez Riverola. 2009. pp. 166-174 (Advances in Soft Computing).

@inproceedings{2a67dbd03bbf4e49a24719620be00c4d,

title = "A recursive genetic algorithm to automatically select genes for cancer classification",

abstract = "Gene expression technology namely microarray, offers the ability to measure the expression levels of thousands of genes simultaneously in a biological organism. Microarray data are expected to be of significant help in the development of efficient cancer diagnosis and classification platform. The main problem that needs to be addressed is the selection of a small subset of genes that contributes to a disease from the thousands of genes measured on microarray that are inherently noisy. Most approaches from previous works have selected the numbers of genes manually and thus, have caused difficulty, especially for beginner biologists. Hence, this paper aims to automatically select a small subset of informative genes that is most relevant for the cancer classification. In order to achieve this aim, a recursive genetic algorithm has been proposed. Experimental results show that the gene subset is small in size and yield better classification accuracy as compared with other previous works as well as four methods experimented in this work. A list of informative genes in the best subsets is also presented for biological usage.",

keywords = "Cancer classification, Gene selection, Genetic Algorithm, Microarray data, Recursive genetic algorithm, Support vector machine",

author = "Mohamad, \{Mohd Saberi\} and Sigeru Omatu and Safaai Deris and Michifumi Yoshioka",

year = "2009",

doi = "10.1007/978-3-540-85861-4\_20",

language = "English",

isbn = "9783540858607",

series = "Advances in Soft Computing",

pages = "166--174",

editor = "Juan Corchado and \{De Paz\}, Juan and Miguel Rocha and Riverola, \{Florentino Fernandez\}",

booktitle = "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)",

}

TY - GEN

T1 - A recursive genetic algorithm to automatically select genes for cancer classification

AU - Mohamad, Mohd Saberi

AU - Omatu, Sigeru

AU - Deris, Safaai

AU - Yoshioka, Michifumi

PY - 2009

Y1 - 2009

N2 - Gene expression technology namely microarray, offers the ability to measure the expression levels of thousands of genes simultaneously in a biological organism. Microarray data are expected to be of significant help in the development of efficient cancer diagnosis and classification platform. The main problem that needs to be addressed is the selection of a small subset of genes that contributes to a disease from the thousands of genes measured on microarray that are inherently noisy. Most approaches from previous works have selected the numbers of genes manually and thus, have caused difficulty, especially for beginner biologists. Hence, this paper aims to automatically select a small subset of informative genes that is most relevant for the cancer classification. In order to achieve this aim, a recursive genetic algorithm has been proposed. Experimental results show that the gene subset is small in size and yield better classification accuracy as compared with other previous works as well as four methods experimented in this work. A list of informative genes in the best subsets is also presented for biological usage.

AB - Gene expression technology namely microarray, offers the ability to measure the expression levels of thousands of genes simultaneously in a biological organism. Microarray data are expected to be of significant help in the development of efficient cancer diagnosis and classification platform. The main problem that needs to be addressed is the selection of a small subset of genes that contributes to a disease from the thousands of genes measured on microarray that are inherently noisy. Most approaches from previous works have selected the numbers of genes manually and thus, have caused difficulty, especially for beginner biologists. Hence, this paper aims to automatically select a small subset of informative genes that is most relevant for the cancer classification. In order to achieve this aim, a recursive genetic algorithm has been proposed. Experimental results show that the gene subset is small in size and yield better classification accuracy as compared with other previous works as well as four methods experimented in this work. A list of informative genes in the best subsets is also presented for biological usage.

KW - Cancer classification

KW - Gene selection

KW - Genetic Algorithm

KW - Microarray data

KW - Recursive genetic algorithm

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/58149116941

UR - https://www.scopus.com/pages/publications/58149116941#tab=citedBy

U2 - 10.1007/978-3-540-85861-4_20

DO - 10.1007/978-3-540-85861-4_20

M3 - Conference contribution

AN - SCOPUS:58149116941

SN - 9783540858607

T3 - Advances in Soft Computing

SP - 166

EP - 174

BT - 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)

A2 - Corchado, Juan

A2 - De Paz, Juan

A2 - Rocha, Miguel

A2 - Riverola, Florentino Fernandez

ER -

A recursive genetic algorithm to automatically select genes for cancer classification

Abstract

Publication series

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this