| 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 | 273 | 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 | 180 | 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 | 116 | 2017 |
| Improving contact prediction along three dimensions C Feinauer, MJ Skwark, A Pagnani, E Aurell PLOS Comp Biol 10 (10), e1003847, 2014 | 89 | 2014 |
| 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 | 69 | 2016 |
| 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 | 37 | 2021 |
| TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes B Meynard-Piganeau, C Feinauer, M Weigt, AM Walczak, T Mora Proceedings of the National Academy of Sciences 121 (24), e2316401121, 2024 | 26 | 2024 |
| 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 | 26 | 2018 |
| 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 | 25 | 2022 |
| Context-aware prediction of pathogenicity of missense mutations involved in human disease C Feinauer, M Weigt arXiv preprint arXiv:1701.07246, 2017 | 19 | 2017 |
| 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 | 19 | 2013 |
| Generating interacting protein sequences using domain-to-domain translation B Meynard-Piganeau, C Fabbri, M Weigt, A Pagnani, C Feinauer Bioinformatics 39 (7), btad401, 2023 | 4 | 2023 |
| Interpretable pairwise distillations for generative protein sequence models C Feinauer, B Meynard-Piganeau, C Lucibello PLOS Computational Biology 18 (6), e1010219, 2022 | 4 | 2022 |
| Mean Dimension of Generative Models for Protein Sequences C Feinauer, E Borgonovo bioRxiv, 2022.12. 12.520028, 2022 | 2 | 2022 |
| The Mean Dimension of Neural Networks--What causes the interaction effects? R Hahn, C Feinauer, E Borgonovo arXiv preprint arXiv:2207.04890, 2022 | 2 | 2022 |
| Reconstruction of pairwise interactions using energy-based models C Feinauer, C Lucibello Mathematical and Scientific Machine Learning, 291-313, 2022 | 2 | 2022 |
| Natural representation of composite data with replicated autoencoders M Negri, D Bergamini, C Baldassi, R Zecchina, C Feinauer arXiv preprint arXiv:1909.13327, 2019 | 2 | 2019 |
| Uncovering sequence diversity from a known protein structure LA Silva, B Meynard-Piganeau, C Lucibello, C Feinauer arXiv preprint arXiv:2406.11975, 2024 | | 2024 |
| The twin peaks of learning neural networks E Demyanenko, C Feinauer, EM Malatesta, L Saglietti Machine Learning: Science and Technology, 2024 | | 2024 |
| THE MEAN DIMENSION OF NEURAL NETWORKS-WHAT R Hahn, C Feinauer, E Borgonovo stat 1050, 11, 2022 | | 2022 |
Martin WeigtSorbonne Université Paris (former Université Pierre & Marie Curie)Zweryfikowany adres z upmc.fr