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llee . dea sites potentiels dans les ARN par discrimination. Les technologies employées reposent sur de~ modèles statistiques comme les machines d'apprentissage statistique telles que les SVM, e;; E,'J[ la recherche algorithmique discrète c'~e mots. Enfin, dans le but de caractériser fonctionnellement ces motifs, nous avons employé une modélisation formelle. Pour ce faire, nous H0US sommes tout d'3.bord focalisé sur la rÉ.gulation d'un si'Ge d'épïssage seul. Ce modèle est fOflflaljsé dans un langage de programmation par contn:lintf.'S concurrentes hybrides (Hybüd cc) et validé qualitativement par rapport aux donn.ées expér~mentales. Il est alors possib18 ~'envisager une modélisation qui intègre les régulation;" d\111 site d'épis- S?vge dans un contExte biologique plus large comme la régulation simultanÉe de plusieurs sites. :C\111 point de vue théorique, ce nouveau modèle toujours forrnalist en Hybrid cc est une b8.'3E' théorique intéressante pour envisager l'étude d'un système concernant plus .i.eu:s échelles biologiques ,