Staff View: A three-stage method to select informative genes for cancer classification

A three-stage method to select informative genes for cancer classification

Microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a small subset of genes from the thousands of genes in the data that contributes to...

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Main Authors:	Mohamad, Mohd. Saberi, Omatu, Sigeru, Yoshioka, Michifumi, Deris, Safaai
Format:	Article
Published:	2010
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/22832/ http://www.academia.edu/1320488/A_THREE-STAGE_METHOD_TO_SELECT_INFORMATIVE_GENES_FOR_CANCER_CLASSIFICATION
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id	my.utm.22832
record_format	eprints
spelling	my.utm.228322018-03-15T01:07:55Z http://eprints.utm.my/id/eprint/22832/ A three-stage method to select informative genes for cancer classification Mohamad, Mohd. Saberi Omatu, Sigeru Yoshioka, Michifumi Deris, Safaai QA Mathematics Microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a small subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (high-dimensional data). In this study, we propose a three-stage gene selection method to select a small subset of informative genes that is most relevant for the cancer classification. It has three stages: 1) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to yield near-optimal gene subsets; 3) analyzing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a small subset of informative genes. The experimental results show that our proposed method is capable in selecting the small subset to obtain better classification accuracies than other related previous works as well as five methods experimented in this work. Additionally, a list of informative genes in the final gene subsets is also presented for biological usage. 2010 Article PeerReviewed Mohamad, Mohd. Saberi and Omatu, Sigeru and Yoshioka, Michifumi and Deris, Safaai (2010) A three-stage method to select informative genes for cancer classification. International Journal of Innovative Computing Information and Control, 6 (1). 117 - 125. ISSN 1349-4198 http://www.academia.edu/1320488/A_THREE-STAGE_METHOD_TO_SELECT_INFORMATIVE_GENES_FOR_CANCER_CLASSIFICATION
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA Mathematics
spellingShingle	QA Mathematics Mohamad, Mohd. Saberi Omatu, Sigeru Yoshioka, Michifumi Deris, Safaai A three-stage method to select informative genes for cancer classification
description	Microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a small subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of samples compared to the huge number of genes (high-dimensional data). In this study, we propose a three-stage gene selection method to select a small subset of informative genes that is most relevant for the cancer classification. It has three stages: 1) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to yield near-optimal gene subsets; 3) analyzing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a small subset of informative genes. The experimental results show that our proposed method is capable in selecting the small subset to obtain better classification accuracies than other related previous works as well as five methods experimented in this work. Additionally, a list of informative genes in the final gene subsets is also presented for biological usage.
format	Article
author	Mohamad, Mohd. Saberi Omatu, Sigeru Yoshioka, Michifumi Deris, Safaai
author_facet	Mohamad, Mohd. Saberi Omatu, Sigeru Yoshioka, Michifumi Deris, Safaai
author_sort	Mohamad, Mohd. Saberi
title	A three-stage method to select informative genes for cancer classification
title_short	A three-stage method to select informative genes for cancer classification
title_full	A three-stage method to select informative genes for cancer classification
title_fullStr	A three-stage method to select informative genes for cancer classification
title_full_unstemmed	A three-stage method to select informative genes for cancer classification
title_sort	three-stage method to select informative genes for cancer classification
publishDate	2010
url	http://eprints.utm.my/id/eprint/22832/ http://www.academia.edu/1320488/A_THREE-STAGE_METHOD_TO_SELECT_INFORMATIVE_GENES_FOR_CANCER_CLASSIFICATION
_version_	1643647410145067008
score	13.211869

A three-stage method to select informative genes for cancer classification

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