In order to analyze transcriptomic data along with prior information in a classification context, we are studying the generation of a prediction rule. Transcriptomic data classically present the expression of thousands of genes (variables) with most often only a hundred experiments (individuals). Consequently classical classification methods are not adapted. The solution usually adopted to address this issue is to sort the variables by their relevance, in order to select those which will be used in the classification process.