Conférence de Rémi Monasson lors du colloquium Data Science Colloquium of the ENS
Searching for interaction networks in proteins: from statistical physics to machine learning, and back.
Over the last century, statistical physics was extremely successful to predict the collective behaviour of many physical systems from detailed knowledge about their microscopic components. However, complex systems, whose properties result from the delicate interplay of many strong and heterogenous interactions, are notoriously difficult to tackle with first-principle approaches. It is therefore tempting to use data to infer adequate microscopic models. I will present some efforts made along this direction for proteins, based on the well-known Potts model of statistical mechanics, with an emphasis on computational and theoretical aspects. I will then show how machine learning, whose unsupervised models encompass the Potts model, can be an inspiring source of new questions for statistical mechanics.
Catégories: Data Science Colloquium
Mot-clés : Biologie, physique, protéine, interaction, Big Data, machine learning, intelligence artificielle
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Institutions : Ecole normale supérieure-PSL
Cursus :
Rémi Monasson est directeur de Recherche CNRS au Laboratoire de Physique Théorique de l'École normale supérieure et chargé de cours à l'École Polytechnique.
Cliquer ICI pour fermerDernière mise à jour : 07/03/2018
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