A multivariate approach for integrating genome-wide expression data and biological knowledge | Zendy

Sek Won Kong | Zendy; William T. Pu | Zendy; Peter J. Park | Zendy

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A multivariate approach for integrating genome-wide expression data and biological knowledge

Author(s) -

Sek Won Kong,

William T. Pu,

Peter J. Park

Publication year - 2006

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/btl401

Subject(s) - subspace topology , univariate , linear subspace , computer science , statistic , multivariate statistics , expression (computer science) , principal component analysis , artificial intelligence , data mining , computational biology , pattern recognition (psychology) , mathematics , statistics , machine learning , biology , programming language , geometry

Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions.

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