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A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data

Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data usi...

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Main Authors: Mohamad, Mohd. Saberi, Omatu, Sigeru, Deris, Safaai, Yoshioka, Michifumi
格式: Article
出版: Institute of Electrical and Electronics Engineers 2011
主題:
QA Mathematics
在線閱讀:http://eprints.utm.my/id/eprint/44690/
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6017123
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spelling my.utm.446902017-01-31T06:53:05Z http://eprints.utm.my/id/eprint/44690/ A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data Mohamad, Mohd. Saberi Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi QA Mathematics Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles’ speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle’s positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of bi- nary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO Institute of Electrical and Electronics Engineers 2011 Article PeerReviewed Mohamad, Mohd. Saberi and Omatu, Sigeru and Deris, Safaai and Yoshioka, Michifumi (2011) A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data. IEEE Transactions on Information Technology in Biomedicine, 15 (6). 813- 822. ISSN 1089-7771 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6017123 DOI:10.1109/TITB.2011.2167756
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
Deris, Safaai
Yoshioka, Michifumi
A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
description Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles’ speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle’s positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of bi- nary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO
format Article
author Mohamad, Mohd. Saberi
Omatu, Sigeru
Deris, Safaai
Yoshioka, Michifumi
author_facet Mohamad, Mohd. Saberi
Omatu, Sigeru
Deris, Safaai
Yoshioka, Michifumi
author_sort Mohamad, Mohd. Saberi
title A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
title_short A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
title_full A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
title_fullStr A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
title_full_unstemmed A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
title_sort modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
publisher Institute of Electrical and Electronics Engineers
publishDate 2011
url http://eprints.utm.my/id/eprint/44690/
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6017123
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score 13.251813

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