title: Multievidence microarray mining.
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.
authors.affiliation: Genomatix Software GmbH, Landsbergerstr. 6, D-80339 München, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Seifert
authors.forename: Matthias
authors.initials: M
authors.lastname: Scherf
authors.forename: Anton
authors.initials: A
authors.lastname: Epple
authors.forename: Thomas
authors.initials: T
authors.lastname: Werner
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005347:Fibroblasts / Q000378:metabolism; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010982:Platelet-Derived Growth Factor / Q000235:genetics* / Q000378:metabolism; D011401:Promoter Regions, Genetic / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics*; D014157:Transcription Factors / Q000235:genetics
chemical_list: D010982:Platelet-Derived Growth Factor; D014157:Transcription Factors