title: Fuzzy clustering analysis of microarray data.
abstract: Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.
authors.affiliation: Department of Computer Science and Engineering, Hohai University, Building 8, No. 2 New Village, West Beijing Road, Apartment 105, Nanjing, Jiangsu, 210008, People's Republic of China. lixinhan2002@yahoo.com.cn
authors.forename: Lixin
authors.initials: L
authors.lastname: Han
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zeng
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome