Chapitre 5 : Approches de la Swarm Intelligence pour la Prédiction de Fonction des
2. Perspectives de recherche
L‘étude expérimentale réalisée nous a permis de déterminer l‘approche bio-inspirée qui nous semble la plus prometteuse pour la prédiction de fonctions des protéines et c‘est dans cette voie que nous souhaitons continuer. Les systèmes immunitaires artificiels représentent une approche bio-inspirée intéressante dont nous pouvons encore approfondir l‘exploitation, en particulier l‘algorithme immunitaire artificiel de reconnaissance (AIRS). Grâce à son procédé de généralisation à partir de peu de données d‘apprentissage, c‘est le plus appropriée pour le problème d‘identification des récepteurs couplés aux protéines G. Ceci s‘explique par le fait qu‘aux niveaux inférieurs de la hiérarchie de la superfamille des RCPGs, on dispose de peu de séquences, ce qui rend la tâche de classification plus ardue, de plus, c‘est à ces niveaux là que le besoin d‘identification se fait le plus ressentir.
Nous avons différentes suggestions qui peuvent, à notre avis, améliorer les performances de l‘algorithme AIRS. Ce dernier utilise l‘approche des k-plus proches voisins pour l‘étape de classification et même si cette approche semble convenir, il est possible d‘implémenter AIRS en utilisant une autre méthode de classification. Nos futurs travaux porteront donc sur l‘évaluation de diverses méthodes de classification afin d‘identifier celle qui apportera le plus de changements significatifs. Ils porteront également sur un changement d‘ensemble de données, car, bien que GDS soit un ensemble de données approprié, il a été collecté en 2007 et depuis, la superfamille des RCPGs a connu quelques modifications, comme celles de la liste des sous-familles et du nombre de séquences. De ce fait, nous projetons de travailler à l‘avenir sur un ensemble de données actualisé que nous construirons nous-mêmes. Aussi, nous souhaitons modifier l‘ensemble des contre-exemples (non-RCPG) car, lorsque nous avons construit l‘ensemble des non-RCPGs, nous avons sélectionné toutes les protéines, membranaires et globulaires confondues. Dans nos prochains travaux, nous allons construire un ensemble de séquences protéiques uniquement membranaires afin d‘affiner le plus possible l‘apprentissage de notre futur modèle et lui permettre ainsi de distinguer les RCPGs (qui sont des protéines membranaires) des autres familles de protéines membranaires.
Bien que les approches de la swarm intelligence que nous avons utilisées ne nous aient pas apporté les résultats escomptés, nous projetons d‘évaluer d‘autres méthodes de cette famille d‘approches bio-inspirées telles que les colonies d‘abeilles artificielles qui figurent parmi les modèles les plus récents de l‘intelligence en essaims et représentent un algorithme de faible complexité, qui a eu beaucoup de succès dans de nombreux domaines d‘application durant la dernière décennie.
Une autre voie de recherche intéressante serait d‘évaluer l‘apport des diverses approches de combinaison de classifieurs bio-inspirés et d‘envisager les différentes possibilités d‘hybridation d‘approches bio-inspirées.
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