title: Clustering gene expression data with kernel principal components.
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.
authors.affiliation: Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21701, USA. zhenqiu@bioanalysis.org
authors.forename: Zhenqiu
authors.initials: Z
authors.lastname: Liu
authors.forename: Dechang
authors.initials: D
authors.lastname: Chen
authors.forename: Halima
authors.initials: H
authors.lastname: Bensmail
authors.forename: Ying
authors.initials: Y
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007938:Leukemia / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism*; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins