Search Results for author: Andrea Pagnani

Found 8 papers, 2 papers with code

Small Coupling Expansion for Multiple Sequence Alignment

no code implementations7 Oct 2022 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 characterizations between homologous sequences in different organisms.

Multiple Sequence Alignment

adabmDCA: Adaptive Boltzmann machine learning for biological sequences

1 code implementation9 Sep 2021 Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi

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.

BIG-bench Machine Learning

Relationship between fitness and heterogeneity in exponentially growing microbial populations

1 code implementation6 Apr 2021 Anna Paola Muntoni, Alfredo Braunstein, Andrea Pagnani, Daniele De Martino, Andrea De Martino

The constrained optimization of evolutionarily-motivated objective functions like the growth rate has emerged as the key theoretical assumption for the study of bacterial metabolism.

Efficient generative modeling of protein sequences using simple autoregressive models

no code implementations4 Mar 2021 Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin Weigt

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 proteins deeply hidden in rapidly growing sequence databases.

Expectation propagation on the diluted Bayesian classifier

no code implementations20 Sep 2020 Alfredo Braunstein, Thomas Gueudré, Andrea Pagnani, Mirko Pieropan

Efficient feature selection from high-dimensional datasets is a very important challenge in many data-driven fields of science and engineering.

Variable Selection

Aligning biological sequences by exploiting residue conservation and coevolution

no code implementations18 May 2020 Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi

Here, we present DCAlign, an efficient alignment algorithm based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include coevolution among positions in a general way, and to be therefore universally applicable to protein- and RNA-sequence alignment without the need of using complementary structural information.

Compressed sensing reconstruction using Expectation Propagation

no code implementations10 Apr 2019 Alfredo Braunstein, Anna Paola Muntoni, Andrea Pagnani, Mirko Pieropan

Many interesting problems in fields ranging from telecommunications to computational biology can be formalized in terms of large underdetermined systems of linear equations with additional constraints or regularizers.

Bayesian Inference

Improving contact prediction along three dimensions

no code implementations3 Mar 2014 Christoph Feinauer, Marcin J. Skwark, Andrea Pagnani, Erik Aurell

Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members.

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