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<dc:title xml:lang="fr">Nouvelle stratégie d'annotation des génomes par l'utilisation d'algorithmes d'intelligence artificielle</dc:title>
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<dcterms:abstract xml:lang="fr">Les projets de séquençage à haut débit produisent une énorme quantité de données biologiques brutes. Cependant, elles sont difficilement exploitables si elles ne sont pas annotées. Pour traiter ces données, des programmes d’annotation de génomes ont été développés, mais ces derniers sont encore trop sujet aux erreurs de prédiction, faisant de l’annotation des génomes un des défis majeurs en bio-informatique. Dans ce contexte, mes travaux de thèse s’organisent autour d’un trinôme : 1) l’amélioration de la prédiction des gènes eucaryotes codant pour des protéines en se focalisant spécifiquement sur les sites d’épissage 2) en exploitant des algorithmes d’intelligence artificielle (CNN et algorithmes évolutionnaires), 3) entraînés avec des données de haute qualité incluant une forte diversité d’espèces eucaryotes. Notre stratégie consiste à combiner l’ensemble des données validées avec les programmes développés afin d’améliorer la prédiction des gènes en diminuant le taux d’erreurs et éviter qu’elles ne se propagent dans les bases de données. De plus, ces travaux permettront une meilleure compréhension des organismes et de leurs mécanismes biologiques.</dcterms:abstract>
<dcterms:abstract xml:lang="en">High-throughput genome sequencing projects produce a huge amount of raw biological data on a daily basis. However, the data are difficult to exploit if they are not annotated. Unfortunately, the currently available genome annotation programs are still too prone to prediction errors, making genome annotation one of the major challenges in bioinformatics. In this context, my thesis is organized around three connected topics: i) improving the prediction of eukaryotic protein-coding genes by focusing on splice sites ii) exploiting artificial intelligence algorithms (convolutional neural networks and evolutionary algorithms), iii) training with high quality data from a wide range of eukaryotic organisms (from humans to protists). The strategy developed consists in combining the processed and validated data set (G3PO) with the developed programs (Spliceator and SpliceSLEIA) in order to improve gene prediction by decreasing the error rate so that they no longer propagated in public databases. This work should contribute to a better understanding of organisms and their biological mechanisms.</dcterms:abstract>
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