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Algorithmes multi-critères pour la prédiction de structures d'ARN

Abstract : Computational RNA structure prediction methods rely on two major algorithmic steps : a sampling step, to propose new structure solutions, and a scoring step to sort the solutions by relevance. A wide diversity of scoring methods exists. Some rely on physical models, some on the similarity to already observed data (so-called data based methods, or knowledge based methods). This thesis proposes structure prediction methods combining two or more scoring criterions, diverse regarding the modelling scale (secondary structure, tertiary structure), their type (theory-based, knowledge-based, compatibility with experimental chemical probing results). The methods describe the Pareto front of the multi-objective optimization problem formed by these criteria. This allows to identify solutions (structures) well scored on each criterion, and to study the correlation between criterions. The presented approaches exploit the latest progresses in the field, like the identification of modules or recurrent interaction networks, and the use of deep learning algorithms. Two neural network architectures (a RNN and a CNN) are adapted from proteins to RNA. A dataset is created to train these networks: RNANet. Two software tools are proposed: the first is called BiORSEO, which predicts the secondary structure based on two criterions (one relative to the structure’s energy, the other relative to the presence of known modules). The second is MOARNA, which predicts coarse-grained 3D structures based on four criterions: energy in 2D and 3D, compatibility with experimental probing results, and with the shape of a known RNA family if one has been identified.
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Submitted on : Monday, November 22, 2021 - 1:43:11 PM
Last modification on : Monday, December 27, 2021 - 9:43:57 PM
Long-term archiving on: : Wednesday, February 23, 2022 - 7:18:50 PM


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  • HAL Id : tel-03440181, version 1


Louis Becquey. Algorithmes multi-critères pour la prédiction de structures d'ARN. Bio-informatique [q-bio.QM]. Université Paris-Saclay; Université d'Evry, 2021. Français. ⟨NNT : 2021UPASG065⟩. ⟨tel-03440181⟩



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