A method to distinguish between co-regulated genes that are up- or down-regulated under a given treatment, based on the composition of the upstrem promoter region, would be a valuable tool in deciphering gene regulatory networks. Ideally, the classification should be based on a small number of regulatory motifs, whos presence in the promoter region of a gene induce a significant effect on its transcriptional regulation. In this paper, we investigate the use of Relevance Vector Machines for this task, and present initial results of an analysis of glucose response in the model plant Arabidopsis thaliana, that has revealed novel biological information.
|Number of pages||6|
|Publication status||Published - Apr 2005|
|Event||Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2005) - Bruges, Belgium|
Duration: 27 Apr 2005 → 29 Apr 2005
|Conference||Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2005)|
|Period||27/04/05 → 29/04/05|