161 citations · 217 across the 5 of their papers we have counts for
6 papers
Towards coevolution-aware ancestral sequence reconstruction
Alya Zeinaty, Leonardo di Bari, Saverio Rossi +3
Ancestral sequence reconstruction (ASR) is a powerful approach for studying molecular evolution and the emergence of protein function. Yet most ASR methods assume that sites evolve…
Eco-evolutionary dynamics of adapting pathogens and host immunity
Pierre Barrat-Charlaix, Richard A. Neher
As pathogens spread in a population of hosts, immunity is built up and the pool of susceptible individuals is depleted. This generates selective pressure, to which many human RNA v…
Global multivariate model learning from hierarchically correlated data
Edwin Rodriguez Horta, Alejandro Lage, Martin Weigt +1
Inverse statistical physics aims at inferring models compatible with a set of empirical averages estimated from a high-dimensional dataset of independently distributed equilibrium…
Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence families
Pierre Barrat-Charlaix, Anna Paola Muntoni, Kai Shimagaki +2
Boltzmann machines (BM) are widely used as generative models. For example, pairwise Potts models (PM), which are instances of the BM class, provide accurate statistical models of f…
How pairwise coevolutionary models capture the collective residue variability in proteins
Matteo Figliuzzi, Pierre Barrat-Charlaix, Martin Weigt
Global coevolutionary models of homologous protein families, as constructed by direct coupling analysis (DCA), have recently gained popularity in particular due to their capacity t…
Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
Pierre Barrat-Charlaix, Matteo Figliuzzi, Martin Weigt
The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statisti…