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Système de recommandation basé sur les réseaux pour l'interprétation de résultats de métabolomique

Abstract : Metabolomics allows large-scale studies of the metabolic profile of an individual, which is representative of its physiological state. Metabolic markers characterising a given condition can be obtained through the comparison of those profiles. Therefore, metabolomics reveals a great potential for the diagnosis as well as the comprehension of mechanisms behind metabolic dysregulations, and to a certain extent the identification of therapeutic targets. However, in order to raise new hypotheses, those applications need to put metabolomics results in the light of global metabolism knowledge. This contextualisation of the results can rely on metabolic networks, which gather all biochemical transformations that can be performed by an organism. The major bottleneck preventing this interpretation stems from the fact that, currently, no single metabolomic approach allows monitoring all metabolites, thus leading to a partial representation of the metabolome. Furthermore, in the context of human health related experiments, metabolomics is usually performed on bio-fluid samples. Consequently, those approaches focus on the footprints left by impacted mechanisms rather than the mechanisms themselves. This thesis proposes a new approach to overcome those limitations, through the suggestion of relevant metabolites, which could fill the gaps in a metabolomics signature. This method is inspired by recommender systems used for several on-line activities, and more specifically the recommendation of users to follow on social networks. This approach has been used for the interpretation of the metabolic signature of the hepatic encephalopathy. It allows highlighting some relevant metabolites, closely related to the disease according to the literature, and led to a better comprehension of the impaired mechanisms and as a result the proposition of new hypothetical scenario. It also improved and enriched the original signature by guiding deeper investigation of the raw data, leading to the addition of missed compounds. Models and data characterisation, alongside technical developments presented in this thesis, can also offer generic frameworks and guidelines for metabolic networks topological analysis.
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Submitted on : Monday, January 21, 2019 - 5:14:17 PM
Last modification on : Tuesday, October 20, 2020 - 11:34:14 AM


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  • HAL Id : tel-01988413, version 1



Clément Frainay. Système de recommandation basé sur les réseaux pour l'interprétation de résultats de métabolomique. Médecine humaine et pathologie. Université Paul Sabatier - Toulouse III, 2017. Français. ⟨NNT : 2017TOU30297⟩. ⟨tel-01988413⟩



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