Multievidence microarray mining.

by: M Seifert, M Scherf, A Epple, T Werner

Trends Genet, Vol. 21, No. 10. (October 2005), pp. 553-558.

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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.

@article{citeulike:1230390, 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.}, address = {Genomatix Software GmbH, Landsbergerstr. 6, D-80339 M\"{u}nchen, Germany.}, author = {Seifert, M. and Scherf, M. and Epple, A. and Werner, T. }, citeulike-article-id = {1230390}, doi = {http://dx.doi.org/10.1016/j.tig.2005.07.011}, issn = {0168-9525}, journal = {Trends Genet}, keywords = {functional\_annotation, microarray}, month = {October}, number = {10}, pages = {553--558}, posted-at = {2008-02-22 13:59:50}, priority = {5}, title = {Multievidence microarray mining.}, url = {http://dx.doi.org/10.1016/j.tig.2005.07.011}, volume = {21}, year = {2005} }

RIS record

TY - JOUR ID - citeulike:1230390 L3 - citeulike-article-id:1230390 TI - Multievidence microarray mining. JF - Trends Genet VL - 21 IS - 10 SP - 553 EP - 558 AD - Genomatix Software GmbH, Landsbergerstr. 6, D-80339 München, Germany. SN - 0168-9525 N2 - 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. KW - functional_annotation KW - microarray AU - Seifert, M AU - Scherf, M AU - Epple, A AU - Werner, T PY - 2005/10// UR - http://dx.doi.org/10.1016/j.tig.2005.07.011 ER -

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