1.6k citations · 6.9k across the 61 of their papers we have counts for
62 papers
Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems
Simone Ciarella, Jeanne Trinquier, Martin Weigt +1
Several strategies have been recently proposed in order to improve Monte Carlo sampling efficiency using machine learning tools. Here, we challenge these methods by considering a c…
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
Carlos A. Gandarilla-Perez, Sergio Pinilla, Anne-Florence Bitbol +1
Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequ…
Statistical-physics approaches to RNA molecules, families and networks
Simona Cocco, Andrea De Martino, Andrea Pagnani +1
This contribution focuses on the fascinating RNA molecule, its sequence-dependent folding driven by base-pairing interactions, the interplay between these interactions and natural…
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes
Juan Rodriguez-Rivas, Giancarlo Croce, Maureen Muscat +1
The emergence of new variants of SARS-CoV-2 is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therap…
adabmDCA: Adaptive Boltzmann machine learning for biological sequences
Anna Paola Muntoni, Andrea Pagnani, Martin Weigt +1
Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are…
Modeling sequence-space exploration and emergence of epistatic signals in protein evolution
Matteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi +1
During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here we build upon recent progress in reconstructing dat…