Christoph Feinauer
Cited by
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Inverse statistical physics of protein sequences: a key issues review
S Cocco, C Feinauer, M Figliuzzi, R Monasson, M Weigt
Reports on Progress in Physics 81 (3), 032601, 2018
Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners
C Baldassi, M Zamparo, C Feinauer, A Procaccini, R Zecchina, M Weigt, ...
PloS one 9 (3), e92721, 2014
Improving contact prediction along three dimensions
C Feinauer, MJ Skwark, A Pagnani, E Aurell
PLOS Comp Biol 10 (10), e1003847, 2014
Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria
A Couce, LV Caudwell, C Feinauer, T Hindré, JP Feugeas, M Weigt, ...
Proceedings of the National Academy of Sciences 114 (43), E9026-E9035, 2017
Inter-protein sequence co-evolution predicts known physical interactions in bacterial ribosomes and the Trp operon
C Feinauer, H Szurmant, M Weigt, A Pagnani
PloS one 11 (2), e0149166, 2016
Architecture of a mammalian glomerular domain revealed by novel volume electroporation using nanoengineered microelectrodes
D Schwarz, M Kollo, C Bosch, C Feinauer, I Whiteley, TW Margrie, ...
Nature communications 9 (1), 183, 2018
Entropic gradient descent algorithms and wide flat minima
F Pittorino, C Lucibello, C Feinauer, G Perugini, C Baldassi, ...
Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 124015, 2021
Zinc finger proteins and the 3D organization of chromosomes
CJ Feinauer, A Hofmann, S Goldt, L Liu, G Mate, DW Heermann
Advances in protein chemistry and structural biology 90, 67-117, 2013
Context-aware prediction of pathogenicity of missense mutations involved in human disease
C Feinauer, M Weigt
arXiv preprint arXiv:1701.07246, 2017
Deep networks on toroids: removing symmetries reveals the structure of flat regions in the landscape geometry
F Pittorino, A Ferraro, G Perugini, C Feinauer, C Baldassi, R Zecchina
International Conference on Machine Learning, 17759-17781, 2022
The Mean Dimension of Neural Networks--What causes the interaction effects?
R Hahn, C Feinauer, E Borgonovo
arXiv preprint arXiv:2207.04890, 2022
Interpretable pairwise distillations for generative protein sequence models
C Feinauer, B Meynard-Piganeau, C Lucibello
PLOS Computational Biology 18 (6), e1010219, 2022
Reconstruction of pairwise interactions using energy-based models
C Feinauer, C Lucibello
Mathematical and Scientific Machine Learning, 291-313, 2022
Mean Dimension of Generative Models for Protein Sequences
C Feinauer, E Borgonovo
bioRxiv, 2022.12. 12.520028, 2022
Generating Interacting Protein Sequences using Domain-to-Domain Translation
B Meynard-Piganeau, C Fabbri, M Weigt, A Pagnani, C Feinauer
bioRxiv, 2022.05. 30.494026, 2022
Context-Aware Generative Models for Multi-Domain Proteins using Transformers
B Meynard-Piganeau, C Fabbri, M Weigt, A Pagnani, C Feinauer
Natural representation of composite data with replicated autoencoders
M Negri, D Bergamini, C Baldassi, R Zecchina, C Feinauer
arXiv preprint arXiv:1909.13327, 2019
The Statistical Mechanics Approach to Protein Sequence Data: Beyond Contact Prediction
C Feinauer
A Binding Model and Similarity for Flexible Modular Proteins
CJ Feinauer, A Hofmann, S Goldt, L Liu, DW Heermann
Bulletin of the American Physical Society 58, 2013
Reconstruction of Pairwise Interactions using Energy-Based Models
C Lucibello, C Feinauer
Energy Based Models Workshop-ICLR 2021, 0
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Articles 1–20