Conférence de Jean-Philippe Vert lors du colloquium Data Science Colloquium of the ENS
This colloquium is organized around data sciences in a broad sense, with the goal of bringing together researchers with diverse backgrounds (including mathematics, computer science, physics, chemistry and neuroscience) but a common interest in dealing with large scale or high dimensional data.
As the cost and throughput of genomic technologies reach a point where DNA sequencing is close to becoming a routine exam at the clinics, there is a lot of hope that treatments of diseases like cancer can dramatically improve by a digital revolution in medicine, where smart algorithms analyze « big medical data » to help doctors take the best decisions for each patient or to suggest new directions for drug development. While artificial intelligence and machine learning-based algorithms have indeed had a great impact on many data-rich fields, their application on genomic data raises numerous computational and mathematical challenges that I will illustrate on a few examples of patient stratification or drug response prediction from genomic data.
Catégories: Data Science Colloquium
Mot-clés : Biologie, ADN, Médecine, cancer, Nouvelles technologies, informatique, pathologie, Big Data, interdisciplinarité, bio-informatique, gène, traitement, code séquentiel, intelligence artificielle
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ENS / Institut Curie / Mines ParisTech
Chercheur en bio-informatique
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Cursus :
Jean-Philippe Vert est directeur du Centre de bio-informatique à Mines ParisTech et directeur adjoint de l’unité « Cancer et génome : bio-informatique, biostatistiques et épidémiologie d'un système complexe » associant Mines ParisTech, l’Institut Curie et l’INSERM.
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