Unsupervised feature selection via two-way ordering in gene expression analysis | Zendy

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Unsupervised feature selection via two-way ordering in gene expression analysis

Author(s) -

Chris Ding

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/btg149

Subject(s) - selection (genetic algorithm) , gene , feature selection , computer science , phenotype , set (abstract data type) , expression (computer science) , similarity (geometry) , computational biology , feature (linguistics) , gene selection , data mining , artificial intelligence , biology , gene expression , genetics , linguistics , philosophy , image (mathematics) , programming language , microarray analysis techniques

Selection of genes most relevant and informative for certain phenotypes is an important aspect in gene expression analysis. Most current methods select genes based on known phenotype information. However, certain set of genes may correspond to new phenotypes which are yet unknown, and it is important to develop novel effective selection methods for their discovery without using any prior phenotype information.

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