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Type: Journal article
Title: A framework for gene expression analysis
Author: Schreiber, A.
Baumann, U.
Citation: Bioinformatics, 2007; 23(2):191-197
Publisher: Oxford Univ Press
Issue Date: 2007
ISSN: 1367-4803
Statement of
Andreas W. Schreiber and Ute Baumann
Abstract: Motivation: 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. Results: It is shown that perturbation expansions familiar from biological systems theory make precise predictions for the types of relationships to be expected for expression profiles of biologically associated genes, even if the underlying biological factors responsible for this association are not known. Classification criteria are derived, most of which are not usually employed in clustering algorithms. The approach is illustrated by using the AtGenExpress Arabidopsis thaliana developmental expression map.
Keywords: Arabidopsis
Arabidopsis Proteins
Oligonucleotide Array Sequence Analysis
Gene Expression Profiling
Signal Transduction
Gene Expression
Gene Expression Regulation, Plant
Models, Biological
Computer Simulation
Provenance: Bioinformatics Advance Access originally published online on November 21, 2006
Rights: Copyright © The Author 2006. Published by Oxford University Press. All rights reserved.
DOI: 10.1093/bioinformatics/btl591
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