Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees | Zendy

Ying Xu | Zendy; Victor Olman | Zendy; Dong Xu | Zendy

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Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees

Author(s) -

Ying Xu,

Victor Olman,

Dong Xu

Publication year - 2002

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/18.4.536

Subject(s) - cluster analysis , computer science , minimum spanning tree , data mining , graph , correlation clustering , spanning tree , cure data clustering algorithm , representation (politics) , theoretical computer science , algorithm , artificial intelligence , mathematics , combinatorics , politics , political science , law

Gene expression data clustering provides a powerful tool for studying functional relationships of genes in a biological process. Identifying correlated expression patterns of genes represents the basic challenge in this clustering problem.

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