Multievidence microarray mining

Microarray mining is a challenging task because of the superposition of several processes in the data. We believe that the combination of microarray data-based analyses (statistical significance analysis of gene expression) with array-independent analyses (literature-mining and promoter analysis) en...

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Bibliographic Details
Published in:Trends in genetics 2005-10, Vol.21 (10), p.553-558
Main Authors: Seifert, Martin, Scherf, Matthias, Epple, Anton, Werner, Thomas
Format: Article
Language:English
Subjects:
Biological and medical sciences
Computational Biology - methods
Databases, Genetic
Fibroblasts - metabolism
Fundamental and applied biological sciences. Psychology
Genetics of eukaryotes. Biological and molecular evolution
Models, Genetic
Molecular and cellular biology
Oligonucleotide Array Sequence Analysis - methods
Platelet-Derived Growth Factor - genetics
Platelet-Derived Growth Factor - metabolism
Promoter Regions, Genetic - genetics
Signal Transduction - genetics
Transcription Factors - genetics
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Summary:Microarray mining is a challenging task because of the superposition of several processes in the data. We believe that the combination of microarray data-based analyses (statistical significance analysis of gene expression) with array-independent analyses (literature-mining and promoter analysis) enables some of the problems of traditional array analysis to be overcome. As a proof-of-principle, we revisited publicly available microarray data derived from an experiment with platelet-derived growth factor (PDGF)-stimulated fibroblasts. Our strategy revealed results beyond the detection of the major metabolic pathway known to be linked to the PDGF response: we were able to identify the crosstalking regulatory networks underlying the metabolic pathway without using a priori knowledge about the experiment.
ISSN:0168-9525
DOI:10.1016/j.tig.2005.07.011