Thu, 21 Aug 2008 15:13:33 BST CiteULike: heliopais Werner http://www.citeulike.org/user/heliopais/author/Werner CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/heliopais/article/1446999 <i>Mechanisms of Ageing and Development, Vol. 128, No. 1. (January 2007), pp. 168-172.</i><br /><br />Gene regulation and aging are intrinsically linked and these links often reach directly to transcription factors and their actions in gene regulation. However, it is very difficult to follow all the individual directions such factors can affect. Therefore, the opposite approach became more popular recently, i.e. observing the endpoints of all these actions. Microarrays are the preferred technology to monitor large-scale changes in transcripts across whole genomes. The trade-off for being able to survey whole genome transcriptomes is that the results are mere observations, which do not directly reveal the underlying mechanisms that represent the real link to transcription factors and their actions. Fortunately, a combination of knowledge mining (including but not restricted to literature mining) with genomics analyses can be harnessed to elucidate at least some of the regulatory networks orchestrating the transcriptional changes observed by microarray experiments. Thus, a considerable part of the functional system structure of cells and organisms can be revealed, which is a pivotal prerequisite for any meaningful systems biology approach towards aging related phenotypes. Regulatory networks: linking microarray data to systems biology. Thomas Werner doi:10.1016/j.mad.2006.11.022 Mechanisms of Ageing and Development, Vol. 128, No. 1. (January 2007), pp. 168-172. 2007-07-10T16:20:01-00:00 2007 Mechanisms of Ageing and Development 0047-6374 128 1 168 172 genetic_regulatory_network microarray http://www.citeulike.org/user/heliopais/article/1230390 <i>Trends Genet, Vol. 21, No. 10. (October 2005), pp. 553-558.</i><br /><br />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. Multievidence microarray mining. M Seifert M Scherf A Epple T Werner doi:10.1016/j.tig.2005.07.011 Trends Genet, Vol. 21, No. 10. (October 2005), pp. 553-558. 2007-04-16T23:06:41-00:00 2005 Trends Genet 0168-9525 21 10 553 558 functional_annotation microarray http://www.citeulike.org/user/heliopais/article/233338 <i>Bioinformatics, Vol. 21, No. 13. (1 July 2005), pp. 2933-2942.</i> MatInspector and beyond: promoter analysis based on transcription factor binding sites K Cartharius K Frech K Grote B Klocke M Haltmeier A Klingenhoff M Frisch M Bayerlein T Werner doi:10.1093/bioinformatics/bti473 Bioinformatics, Vol. 21, No. 13. (1 July 2005), pp. 2933-2942. 2005-06-21T10:29:28-00:00 2005 Bioinformatics 1367-4803 21 13 2933 2942 Oxford University Press binding_site_prediction transcription_factor