A framework for gene expression analysis
Date
2007
Authors
Schreiber, A.
Baumann, U.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Bioinformatics, 2007; 23(2):191-197
Statement of Responsibility
Andreas W. Schreiber and Ute Baumann
Conference Name
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.
School/Discipline
Dissertation Note
Provenance
Bioinformatics Advance Access originally published online on November 21, 2006
Description
Access Status
Rights
Copyright © The Author 2006. Published by Oxford University Press. All rights reserved.