title: Semi-automated clustering of gene expression data sets.
abstract: Clustering, as one of key analysis tools for gene expression data sets, attempts to discover groups of genes having similar expression patterns. In order to get a reasonable biological interpretation, it is desirable that a clustering result be accurate enough. However, conventional clustering methods do not always meet this demand since they require the exact tuning of input parameters and cluster centers for an acceptable quality of result. Through an intuitive user interaction, UI-Cluster solves the problem mentioned above, and yields better clustering results.
authors.affiliation: Electronics and Telecommunications Research Institute, 161, Gajeong-dong, Yuseong-gu, Daejeon, 305-700, Republic of KOREA. kimmh@etri.re.kr
authors.forename: Minho
authors.initials: M
authors.lastname: Kim
authors.forename: Ho-Youl
authors.initials: HY
authors.lastname: Jung
authors.forename: Myungguen
authors.initials: M
authors.lastname: Chung
authors.forename: Pora
authors.initials: P
authors.lastname: Kim
authors.forename: Seon-Hee
authors.initials: SH
authors.lastname: Park
authors.forename: Soo-Jun
authors.initials: SJ
authors.lastname: Park
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software