title: Clustering methods for microarray gene expression data.
abstract: Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.
authors.affiliation: National Research Council Canada, Institute for Information Technology, Scientific Park, Moncton, New Brunswick, Canada. Nabil.Belacel@nrc-cnrc.gc.ca
authors.forename: Nabil
authors.initials: N
authors.lastname: Belacel
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.forename: Miroslava
authors.initials: M
authors.lastname: Cuperlovic-Culf
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*