Search Results - "Feinauer, Christoph"

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  1. 1

    Inverse statistical physics of protein sequences: a key issues review by Cocco, Simona, Feinauer, Christoph, Figliuzzi, Matteo, Monasson, Rémi, Weigt, Martin

    Published in Reports on progress in physics (01-03-2018)
    “…In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their…”
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    Journal Article
  2. 2

    Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon by Feinauer, Christoph, Szurmant, Hendrik, Weigt, Martin, Pagnani, Andrea

    Published in PloS one (16-02-2016)
    “…Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a…”
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    Journal Article
  3. 3

    Interpretable pairwise distillations for generative protein sequence models by Feinauer, Christoph, Meynard-Piganeau, Barthelemy, Lucibello, Carlo

    Published in PLoS computational biology (01-06-2022)
    “…Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects,…”
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    Journal Article
  4. 4

    Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners by Baldassi, Carlo, Zamparo, Marco, Feinauer, Christoph, Procaccini, Andrea, Zecchina, Riccardo, Weigt, Martin, Pagnani, Andrea

    Published in PloS one (24-03-2014)
    “…In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong…”
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    Journal Article
  5. 5

    Improving contact prediction along three dimensions by Feinauer, Christoph, Skwark, Marcin J, Pagnani, Andrea, Aurell, Erik

    Published in PLoS computational biology (01-10-2014)
    “…Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their…”
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    Journal Article
  6. 6

    The twin peaks of learning neural networks by Demyanenko, Elizaveta, Feinauer, Christoph, Malatesta, Enrico M, Saglietti, Luca

    Published in Machine learning: science and technology (01-06-2024)
    “…Recent works demonstrated the existence of a double-descent phenomenon for the generalization error of neural networks, where highly overparameterized models…”
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    Journal Article
  7. 7

    Generating interacting protein sequences using domain-to-domain translation by Meynard-Piganeau, Barthelemy, Fabbri, Caterina, Weigt, Martin, Pagnani, Andrea, Feinauer, Christoph

    Published in Bioinformatics (Oxford, England) (01-07-2023)
    “…Abstract Motivation Being able to artificially design novel proteins of desired function is pivotal in many biological and biomedical applications. Generative…”
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    Journal Article
  8. 8

    TULIP — a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors that generalizes to unseen epitopes by Meynard-Piganeau, Barthelemy, Feinauer, Christoph, Weigt, Martin, Walczak, Aleksandra, Mora, Thierry

    “…The accurate prediction of binding between T-cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and…”
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    Journal Article
  9. 9

    Zinc Finger Proteins and the 3D Organization of Chromosomes by Feinauer, Christoph J., Hofmann, Andreas, Goldt, Sebastian, Liu, Lei, Máté, Gabriell, Heermann, Dieter W.

    “…Zinc finger domains are one of the most common structural motifs in eukaryotic cells, which employ the motif in some of their most important proteins…”
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    Book Chapter Reference Journal Article
  10. 10

    TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes by Meynard-Piganeau, Barthelemy, Feinauer, Christoph, Weigt, Martin, Walczak, Aleksandra M, Mora, Thierry

    “…The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and…”
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    Journal Article
  11. 11

    Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria by Couce, Alejandro, Caudwell, Larissa Viraphong, Feinauer, Christoph, Hindré, Thomas, Feugeas, Jean-Paul, Weigt, Martin, Lenski, Richard E., Schneider, Dominique, Tenaillon, Olivier

    “…Understanding the extreme variation among bacterial genomes remains an unsolved challenge in evolutionary biology, despite long-standing debate about the…”
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    Journal Article
  12. 12

    Improving Contact Prediction along Three Dimensions: e1003847 by Feinauer, Christoph, Skwark, Marcin J, Pagnani, Andrea, Aurell, Erik

    Published in PLoS computational biology (01-10-2014)
    “…Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their…”
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    Journal Article
  13. 13

    Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon: e0149166 by Feinauer, Christoph, Szurmant, Hendrik, Weigt, Martin, Pagnani, Andrea

    Published in PloS one (01-02-2016)
    “…Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a…”
    Get full text
    Journal Article
  14. 14

    Reconstruction of Pairwise Interactions using Energy-Based Models by Feinauer, Christoph, Lucibello, Carlo

    Published 11-12-2020
    “…Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics…”
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    Journal Article
  15. 15

    Uncovering sequence diversity from a known protein structure by Silva, Luca Alessandro, Meynard-Piganeau, Barthelemy, Lucibello, Carlo, Feinauer, Christoph

    Published 17-06-2024
    “…We present InvMSAFold, a method for generating a diverse set of protein sequences that fold into a single structure. For a given structure, InvMSAFold defines…”
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    Journal Article
  16. 16

    The Mean Dimension of Neural Networks -- What causes the interaction effects? by Hahn, Roman, Feinauer, Christoph, Borgonovo, Emanuele

    Published 11-07-2022
    “…Owen and Hoyt recently showed that the effective dimension offers key structural information about the input-output mapping underlying an artificial neural…”
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    Journal Article
  17. 17

    The twin peaks of learning neural networks by Demyanenko, Elizaveta, Feinauer, Christoph, Malatesta, Enrico M, Saglietti, Luca

    Published 23-01-2024
    “…Recent works demonstrated the existence of a double-descent phenomenon for the generalization error of neural networks, where highly overparameterized models…”
    Get full text
    Journal Article
  18. 18

    Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners: e92721 by Baldassi, Carlo, Zamparo, Marco, Feinauer, Christoph, Procaccini, Andrea, Zecchina, Riccardo, Weigt, Martin, Pagnani, Andrea

    Published in PloS one (01-03-2014)
    “…In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong…”
    Get full text
    Journal Article
  19. 19

    Context-Aware Prediction of Pathogenicity of Missense Mutations Involved in Human Disease by Feinauer, Christoph, Weigt, Martin

    Published 25-01-2017
    “…Amino-acid substitutions are implicated in a wide range of human diseases, many of which are lethal. Distinguishing such mutations from polymorphisms without…”
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    Journal Article
  20. 20

    Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry by Pittorino, Fabrizio, Ferraro, Antonio, Perugini, Gabriele, Feinauer, Christoph, Baldassi, Carlo, Zecchina, Riccardo

    Published 16-06-2022
    “…We systematize the approach to the investigation of deep neural network landscapes by basing it on the geometry of the space of implemented functions rather…”
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    Journal Article