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Theses

Identification de signature causale pathologie par intégration de données multi-omiques

Méline Wery 1, 2
1 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Systematic erythematosus lupus is an example of a complex, heterogeneous and multifactorial disease. The identification of signature that can explain the cause of a disease remains an important challenge for the stratification of patients. Classic statistical analysis can hardly be applied when population of interest are heterogeneous and they do not highlight the cause. This thesis presents two methods that answer those issues. First, a transomic model is described in order to structure all the omic data, using semantic Web (RDF). Its supplying is based on a patient-centric approach. SPARQL query interrogates this model and allow the identification of expression Individually-Consistent Trait Loci (eICTLs). It a reasoning association between a SNP and a gene whose the presence of the SNP impact the variation of its gene expression. Those elements provide a reduction of omics data dimension and show a more informative contribution than genomic data. This first method are omics data-driven. Then, the second method is based on the existing regulation dependancies in biological networks. By combining the dynamic of biological system with the formal concept analysis, the generated stable states are automatically classified. This classification enables the enrichment of biological signature, which caracterised a phenotype. Moreover, new hybrid phenotype is identified.
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Submitted on : Friday, April 30, 2021 - 10:17:07 AM
Last modification on : Wednesday, November 3, 2021 - 8:09:24 AM

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  • HAL Id : tel-03213016, version 2

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Méline Wery. Identification de signature causale pathologie par intégration de données multi-omiques. Bio-informatique [q-bio.QM]. Université Rennes 1, 2020. Français. ⟨NNT : 2020REN1S071⟩. ⟨tel-03213016v2⟩

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