SamCluster: an integrated scheme for automatic discovery of sample classes using gene expression profile | Zendy

Wuju Li | Zendy; Ming Fan | Zendy; Momiao Xiong | Zendy

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SamCluster: an integrated scheme for automatic discovery of sample classes using gene expression profile

Author(s) -

Wuju Li,

Ming Fan,

Momiao Xiong

Publication year - 2003

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/btg095

Subject(s) - feature selection , cluster analysis , sample (material) , feature (linguistics) , hierarchical clustering , sample size determination , selection (genetic algorithm) , pattern recognition (psychology) , false discovery rate , data mining , computer science , statistical hypothesis testing , computational biology , mathematics , artificial intelligence , biology , gene , statistics , genetics , linguistics , chemistry , philosophy , chromatography

Feature (gene) selection can dramatically improve the accuracy of gene expression profile based sample class prediction. Many statistical methods for feature (gene) selection such as stepwise optimization and Monte Carlo simulation have been developed for tissue sample classification. In contrast to class prediction, few statistical and computational methods for feature selection have been applied to clustering algorithms for pattern discovery.

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