Integrative data science

Diabetes and its complications result from a cascade of complex cellular events, themselves caused by multiple and potentially interacting molecular events. High-throughput biology, notably with the Next-Generation-Sequencing (NGS), now provides a unique opportunity to characterize these events, thus generating large datasets. These data include multi-omics, in particular genetic, epigenetic, transcriptomic and metabolomic, as well as cellular phenotypes (single-cell omics and immunophenotypes). Metadata including clinical features and environmental records (e.g. nutrition) must also be accounted for.
The purpose of the Integrative Biology group is to analyze the multiple datasets jointly to better understand the pathogenesis of diabetes and the different genetic and genomic factors at play in its etiology and complications.
To achieve this goal, the group actions are 1) to follow the lab’s cohort overtime to identify the most relevant clinical features, 2) to help set up the experimental design of the lab omics projects, and 3) to analyze the data with both computational and biostatistical approaches
Specific objectives:
• Genetic and genomic factors implicated in the etiology of diabetes and their complications
• Biomarkers for the diagnosis and care of diabetic patients
• Ultimately precision medicine

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