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Found 10 papers, 3 papers with code
Transition paths in Potts-like energy landscapes: general properties and application to protein sequence models
We study transition paths in energy landscapes over multi-categorical Potts configurations using the mean-field approach introduced by Mauri et al., {\em Phys Rev Lett 130, 158402 (2023)}.
Disentangling representations in Restricted Boltzmann Machines without adversaries
A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties.
Mutational paths with sequence-based models of proteins: from sampling to mean-field characterisation
Identifying and characterizing mutational paths is an important issue in evolutionary biology and in bioengineering.
Barriers and Dynamical Paths in Alternating Gibbs Sampling of Restricted Boltzmann Machines
Restricted Boltzmann Machines (RBM) are bi-layer neural networks used for the unsupervised learning of model distributions from data.
Survival probability and size of lineages in antibody affinity maturation
We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and initial population size.
Inferring epistasis from genomic data with comparable mutation and outcrossing rate
We consider a population evolving due to mutation, selection and recombination, where selection includes single-locus terms (additive fitness) and two-loci terms (pairwise epistatic fitness).
'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
Distributions of data or sensory stimuli often enjoy underlying invariances.
Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
A Restricted Boltzmann Machine (RBM) is an unsupervised machine-learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features.
Learning protein constitutive motifs from sequence data
Statistical analysis of evolutionary-related protein sequences provides insights about their structure, function, and history.
Inverse Statistical Physics of Protein Sequences: A Key Issues Review
In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved.
