AI for Health

🇬🇧 Big data, AI and multiOmics analyses at the single-cell level for diagnostic of primary immunodeficiencies

Nov 10 16:05 - 16:35

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To investigate in an unbiased way the complexity and the heterogeneity among inflammatory responses in general and interferonopathies in particular, our group at the Imagine Institute in Paris, propose a new and unbiased approach, combining two emerging and exciting fields (machine learning algorithm and “big Data” generation at the single-cell level), to better understand the links between mutations in genes and clinical symptoms. Our first objective is to better characterize and stratify patients in more homogeneous groups, by performing state of the art single-cell transcriptomic analysis and clustering of peripheral blood mononuclear cells (PBMC) from different patients suffering from interferonopathies. Then based on artificial intelligence we are aiming towards inferring networks of genes interactions to get a deeper and unbiased understanding of the diversity of the molecular mechanisms behind inflammation. Once established, this combined approach of single-cell coupled with Network inference, could have a tremendous impact on patients at various different levels (better screening, stratification, diagnosis of patients and discovery of new therapeutics) and will be moving the medical research field toward a more personalized medicine.