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- Centre National de la Recherche ScientifiqueFR66 papers
- Sorbonne UniversitéFR61 papers
- Institut de Biologie Paris-SeineFR32 papers
- Université Pierre-et-Marie-CurieFR23 papers
- École Normale Supérieure - PSLFR17 papers
- Laboratoire de Physique de l'Ecole Normale SupérieureFR16 papers
- Politecnico di TorinoIT16 papers
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- Italian institute for Genomic MedicineIT12 papers
- Laboratoire Jacques-Louis LionsFR11 papers
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7 papers · 1 filter
Statistical physics of interacting proteins: impact of dataset size and quality assessed in synthetic sequences
Carlos A. Gandarilla-Pérez, Pierre Mergny, Martin Weigt +1
Identifying protein-protein interactions is crucial for a systems-level understanding of the cell. Recently, algorithms based on inverse statistical physics, e.g. Direct Coupling A…
Inter-residue, inter-protein and inter-family coevolution: bridging the scales
Hendrik Szurmant, Martin Weigt
Interacting proteins coevolve at multiple but interconnected scales, from the residue-residue over the protein-protein up to the family-family level. The recent accumulation of eno…
Adaptive Cluster Expansion for Ising spin models
Simona Cocco, Giancarlo Croce, Francesco Zamponi
We propose an algorithm to obtain numerically approximate solutions of the direct Ising problem, that is, to compute the free energy and the equilibrium observables of spin systems…
Phylogenetic correlations can suffice to infer protein partners from sequences
Guillaume Marmier, Martin Weigt, Anne-Florence Bitbol
Determining which proteins interact together is crucial to a systems-level understanding of the cell. Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-…
Selection of sequence motifs and generative Hopfield-Potts models for protein familiesilies
Kai Shimagaki, Martin Weigt
Statistical models for families of evolutionary related proteins have recently gained interest: in particular pairwise Potts models, as those inferred by the Direct-Coupling Analys…
Learning a Local Symmetry with Neural-Networks
Aurélien Decelle, Victor Martin-Mayor, Beatriz Seoane
We explore the capacity of neural networks to detect a symmetry with complex local and non-local patterns : the gauge symmetry Z 2 . This symmetry is present in physical problems f…