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Inverse statistical physics of protein sequences: a key issues review

Reports on progress in physics, 2018-03, Vol.81 (3), p.032601-032601

2018

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Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural Networks

Physical review letters, 2020-01, Vol.124 (4), p.048302-048302, Article 048302

2020

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Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers

eLife, 2020-06, Vol.9

2020

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Mutational Paths with Sequence-Based Models of Proteins: From Sampling to Mean-Field Characterization

Physical review letters, 2023-04, Vol.130 (15), p.158402-158402, Article 158402

2023

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Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural Networks

Neural computation, 2021-03, Vol.33 (4), p.1063-1112

2021

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Small-correlation expansions for the inverse Ising problem

Journal of physics. A, Mathematical and theoretical, 2009-02, Vol.42 (5), p.055001-055001 (17)

2009

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Improving sequence-based modeling of protein families using secondary-structure quality assessment

Bioinformatics (Oxford, England), 2021-11, Vol.37 (22), p.4083-4090

2021

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Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins

Neural computation, 2019-08, Vol.31 (8), p.1671-1717

2019

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Integration and multiplexing of positional and contextual information by the hippocampal network

PLoS computational biology, 2018-08, Vol.14 (8), p.e1006320-e1006320

2018

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Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods

Proceedings of the National Academy of Sciences - PNAS, 2009-08, Vol.106 (33), p.14058-14062

2009

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Adaptation of olfactory receptor abundances for efficient coding

eLife, 2019-02, Vol.8

2019

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Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment

PLoS computational biology, 2023-11, Vol.19 (11), p.e1011621-e1011621

2023

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Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies

Current opinion in structural biology, 2023-06, Vol.80, p.102571-102571, Article 102571

2023

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A synaptic signal for novelty processing in the hippocampus

Nature communications, 2022-07, Vol.13 (1), p.4122-4122, Article 4122

2022

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Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

Nucleic acids research, 2015-12, Vol.43 (21), p.10444

2015

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Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data

PLoS computational biology, 2023-10, Vol.19 (10), p.e1011521-e1011521

2023

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Estimating the principal components of correlation matrices from all their empirical eigenvectors

Europhysics letters, 2015-12, Vol.112 (5), p.50001-50001

2015

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Disentangling Representations in Restricted Boltzmann Machines without Adversaries

Physical review. X, 2023-04, Vol.13 (2), p.021003, Article 021003

2023

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Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models

PLoS computational biology, 2016-05, Vol.12 (5), p.e1004889-e1004889

2016

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A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

eLife, 2023-09, Vol.12

2023

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Sensorimotor computation underlying phototaxis in zebrafish

Nature communications, 2017-09, Vol.8 (1), p.651-12, Article 651

2017

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'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space

Journal of physics. A, Mathematical and theoretical, 2020-05, Vol.53 (17), p.174002

2020

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Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection

PLoS computational biology, 2022-09, Vol.18 (9), p.e1010561-e1010561

2022

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Emergence of time persistence in a data-driven neural network model

eLife, 2023-03, Vol.12

2023

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Probing T-cell response by sequence-based probabilistic modeling

PLoS computational biology, 2021-09, Vol.17 (9), p.e1009297-e1009297

2021

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Inverse statistical physics of protein sequences: a key issues review

Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural Networks

Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers

Mutational Paths with Sequence-Based Models of Proteins: From Sampling to Mean-Field Characterization

Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural Networks

Small-correlation expansions for the inverse Ising problem

Improving sequence-based modeling of protein families using secondary-structure quality assessment

Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins

Integration and multiplexing of positional and contextual information by the hippocampal network

Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods

Adaptation of olfactory receptor abundances for efficient coding

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment

Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies

A synaptic signal for novelty processing in the hippocampus

Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data

Estimating the principal components of correlation matrices from all their empirical eigenvectors

Disentangling Representations in Restricted Boltzmann Machines without Adversaries

Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Sensorimotor computation underlying phototaxis in zebrafish

'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection

Emergence of time persistence in a data-driven neural network model

Probing T-cell response by sequence-based probabilistic modeling

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