Evaluation and optimization of clustering in gene expression data analysis | Zendy

A. Fazel Famili | Zendy; Ganming Liu | Zendy; Ziying Liu | Zendy

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Evaluation and optimization of clustering in gene expression data analysis

Author(s) -

A. Fazel Famili,

Ganming Liu,

Ziying Liu

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth124

Subject(s) - cluster analysis , computer science , partition (number theory) , identification (biology) , data mining , stability (learning theory) , cluster (spacecraft) , expression (computer science) , gene , computational biology , consensus clustering , artificial intelligence , pattern recognition (psychology) , machine learning , biology , genetics , correlation clustering , mathematics , cure data clustering algorithm , botany , combinatorics , programming language

A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis.

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