Gene expression data classification with kernel principal component analysis

Zhenqiu Liu, Dechang Chen, Halima Bensmail

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

Original languageEnglish
Pages (from-to)155-159
Number of pages5
JournalJournal of Biomedicine and Biotechnology
Volume2005
Issue number2
DOIs
Publication statusPublished - 30 Jun 2005
Externally publishedYes

Fingerprint

Principal Component Analysis
Gene expression
Principal component analysis
Gene Expression
Microarray Analysis
Microarrays
Support vector machines
Logistics
Tumors
Statistical methods
Genes
Logistic Models
Neural networks
Neoplasms

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Genetics
  • Applied Microbiology and Biotechnology

Cite this

Gene expression data classification with kernel principal component analysis. / Liu, Zhenqiu; Chen, Dechang; Bensmail, Halima.

In: Journal of Biomedicine and Biotechnology, Vol. 2005, No. 2, 30.06.2005, p. 155-159.

Research output: Contribution to journalArticle

@article{28864bcb46ba4103a067531dbd586540,
title = "Gene expression data classification with kernel principal component analysis",
abstract = "One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.",
author = "Zhenqiu Liu and Dechang Chen and Halima Bensmail",
year = "2005",
month = "6",
day = "30",
doi = "10.1155/JBB.2005.155",
language = "English",
volume = "2005",
pages = "155--159",
journal = "BioMed Research International",
issn = "2314-6133",
publisher = "Hindawi Publishing Corporation",
number = "2",

}

TY - JOUR

T1 - Gene expression data classification with kernel principal component analysis

AU - Liu, Zhenqiu

AU - Chen, Dechang

AU - Bensmail, Halima

PY - 2005/6/30

Y1 - 2005/6/30

N2 - One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

AB - One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

UR - http://www.scopus.com/inward/record.url?scp=27744594238&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=27744594238&partnerID=8YFLogxK

U2 - 10.1155/JBB.2005.155

DO - 10.1155/JBB.2005.155

M3 - Article

C2 - 16046821

AN - SCOPUS:27744594238

VL - 2005

SP - 155

EP - 159

JO - BioMed Research International

JF - BioMed Research International

SN - 2314-6133

IS - 2

ER -