Clustering gene expression data with kernel principal components

J Bioinform Comput Biol. 2005 Apr;3(2):303-16. doi: 10.1142/s0219720005001168.

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.

Publication types

  • Comparative Study
  • Evaluation Study
  • Validation Study

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Biomarkers, Tumor / genetics
  • Biomarkers, Tumor / metabolism*
  • Cluster Analysis
  • Fuzzy Logic
  • Gene Expression Profiling / methods*
  • Humans
  • Leukemia / diagnosis
  • Leukemia / genetics
  • Leukemia / metabolism*
  • Neoplasm Proteins / genetics
  • Neoplasm Proteins / metabolism*
  • Oligonucleotide Array Sequence Analysis / methods*
  • Pattern Recognition, Automated / methods*
  • Principal Component Analysis

Substances

  • Biomarkers, Tumor
  • Neoplasm Proteins