Graph-based consensus clustering for class discovery from gene expression data | Zendy

Zhiwen Yu | Zendy; Hau−San Wong | Zendy; Hongqiang Wang | Zendy

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Graph-based consensus clustering for class discovery from gene expression data

Author(s) -

Zhiwen Yu,

Hau−San Wong,

Hongqiang Wang

Publication year - 2007

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/btm463

Subject(s) - cluster analysis , computer science , rand index , data mining , scalability , robustness (evolution) , microarray analysis techniques , graph , gene chip analysis , consensus clustering , dna microarray , correlation clustering , artificial intelligence , cure data clustering algorithm , theoretical computer science , gene , biology , gene expression , database , biochemistry

Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stability, scalability and parallelization of the clustering algorithms. By consensus clustering, one can discover the underlying classes of the samples in gene expression data.

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