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AgeWa: an integrated approach for antisense experiment design. | LitMetric

AI Article Synopsis

  • The Human Genome Project has significantly advanced our understanding of disease molecular mechanisms, aiding in drug design through the identification and interaction analysis of relevant genes.
  • New gene expression analysis methods, like microarray technology, enable the evaluation of multiple genes simultaneously but require further validation using in vitro or in vivo models to confirm gene functions.
  • The paper introduces Automatic Gene Walk (AgeWa), a novel tool combining neural filtering and database mining, to enhance the selection of targets for antisense oligonucleotide (ASO) experiments, addressing the lack of standardized procedures in target selection.

Article Abstract

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.

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Source
http://dx.doi.org/10.1109/tnb.2003.809462DOI Listing

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