Semi-automated clustering of gene expression data sets.
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.
Kim
Minho
M
Electronics and Telecommunications Research Institute, 161, Gajeong-dong, Yuseong-gu, Daejeon, 305-700, Republic of KOREA. kimmh@etri.re.kr
Jung
Ho-Youl
HY
Chung
Myungguen
M
Kim
Pora
P
Park
Seon-Hee
SH
Park
Soo-Jun
SJ
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