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Multievidence microarray mining.

Martin Seifert·Matthias Scherf·Anton Epple·Thomas Werner
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Abstract

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.

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Computational BiologyDatabases, GeneticFibroblastsModels, GeneticOligonucleotide Array Sequence AnalysisPlatelet-Derived Growth FactorPromoter Regions, GeneticSignal TransductionTranscription FactorsComparative StudyJournal ArticleResearch Support, Non-U.S. Gov't

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Martin Seifert, Matthias Scherf, Anton Epple, Thomas Werner. 2005. Multievidence microarray mining.. https://doi.org/10.1016/j.tig.2005.07.011

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