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One of the major fallouts of the human genome project relates to the investigation of the molecular mechanisms of diseases. Identification of genes which are involved in a specific pathological process and characterization of their interactions is of fundamental importance for supporting the drug design processes. Discovery of targets and the related experimental validation is a critical step in the development of new drugs. The new experimental methods for gene expression analysis, such as microarray technology, allows for the concurrent evaluation of the expression of multiple genes. The outcome of these new experimental methods requires a subsequent validation of the gene function by using in vitro or in vivo models. In the last decade, one of the most promising methodologies for the investigation of gene function relies upon antisense oligonucleotides (ASO). The crucial step in antisense experiment design is the characterization of the nucleotide domains that can efficiently be targeted by this kind of synthetic molecule. At present, no standardized procedures for target selection are available. In this paper, we propose an integrative approach to ASO target selection: the proposed tool Automatic Gene Walk (AgeWa) combines a neural filter with database mining for the prediction of the optimal target for antisense action.