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A framework for gene expression analysis
Author(s) -
Andreas Schreiber,
Ute Baumann
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/btl591
Subject(s) - cluster analysis , computational biology , expression (computer science) , gene expression , gene , computer science , similarity (geometry) , biology , biological data , microarray analysis techniques , function (biology) , data mining , artificial intelligence , bioinformatics , genetics , image (mathematics) , programming language
Global gene expression measurements as obtained, for example, in microarray experiments can provide important clues to the underlying transcriptional control mechanisms and network structure of a biological cell. In the absence of a detailed understanding of this gene regulation, current attempts at classification of expression data rely on clustering and pattern recognition techniques employing ad-hoc similarity criteria. To improve this situation, a better understanding of the expected relationships between expression profiles of genes associated by biological function is required.

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