Clustering gene expression data with kernel principal components

Zhenqiu Liu, Dechang Chen, Halima Bensmail, Ying Xu

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with microarray data show that the proposed algorithms is in general superior to traditional algorithms.

Original languageEnglish
Pages (from-to)303-316
Number of pages14
JournalJournal of Bioinformatics and Computational Biology
Volume3
Issue number2
DOIs
Publication statusPublished - 1 Apr 2005
Externally publishedYes

Fingerprint

Principal Component Analysis
Gene expression
Principal component analysis
Cluster Analysis
Microarrays
Gene Expression
Experiments

Keywords

  • Fuzzy C-means
  • Kernel principal component analysis
  • Microarray experiment
  • Unsupervised learning

ASJC Scopus subject areas

  • Medicine(all)
  • Cell Biology

Cite this

Clustering gene expression data with kernel principal components. / Liu, Zhenqiu; Chen, Dechang; Bensmail, Halima; Xu, Ying.

In: Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, 01.04.2005, p. 303-316.

Research output: Contribution to journalArticle

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