Clustering gene expression data with kernel principal components.
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.
Liu
Zhenqiu
Z
Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21701, USA. zhenqiu@bioanalysis.org
Chen
Dechang
D
Bensmail
Halima
H
Xu
Ying
Y
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
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins