Méline Wéry’s PhD defense: 16th december 14:00
Méline Wéry’s PhD defense on “Identification of causal pathologic signature by multi-omic data integration” will take place on Thursday 16th december at 14:00 (UTC+1).
The defense will be broadcasted live on youtube at: https://youtu.be/5aL92INe2NI
– Emmanuelle BECKER (Université de Rennes 1)
– Charles BETTEMBOURG (Sanofi, Chilly-Mazarin)
– Laurence CALZONE (Institut Curie, Paris)
– Olivier DAMERON (Université de Rennes 1)
– Franck DELAPLACE (Université de Paris-Saclay, Évry)
– Fleur MOUGIN (LaBRI Bordeaux)
– Anne SIEGEL (IRISA Rennes)
– Vassili SOUMELIS (Hôpital Saint Louis, Paris)
– Emmanuel OGER (Université de Rennes 1)
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.