1.6k citations · 3.4k across the 49 of their papers we have counts for
48 papers
DCAlign v1.0: Aligning biological sequences using co-evolution models and informed priors
Anna Paola Muntoni, Andrea Pagnani
DCAlign is a new alignment method able to cope with the conservation and the co-evolution signals that characterize the columns of multiple sequence alignments of homologous sequen…
Small Coupling Expansion for Multiple Sequence Alignment
Louise Budzynski, Andrea Pagnani
The alignment of biological sequences such as DNA, RNA, and proteins, is one of the basic tools that allow to detect evolutionary patterns, as well as functional/structural charact…
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…
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…
Relationship between fitness and heterogeneity in exponentially growing microbial populations
Anna Paola Muntoni, Alfredo Braunstein, Andrea Pagnani +2
Despite major environmental and genetic differences, microbial metabolic networks are known to generate consistent physiological outcomes across vastly different organisms. This re…
Efficient generative modeling of protein sequences using simple autoregressive models
Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani +2
Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about…