| Emergence of compositional representations in restricted Boltzmann machines J Tubiana, R Monasson Physical review letters 118 (13), 138301, 2017 | 120 | 2017 |
| Learning protein constitutive motifs from sequence data J Tubiana, S Cocco, R Monasson Elife 8, e39397, 2019 | 116 | 2019 |
| ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction J Tubiana, D Schneidman-Duhovny, HJ Wolfson Nature Methods 19 (6), 730-739, 2022 | 93 | 2022 |
| RBM-MHC: a semi-supervised machine-learning method for sample-specific prediction of antigen presentation by HLA-I alleles B Bravi, J Tubiana, S Cocco, R Monasson, T Mora, AM Walczak Cell systems 12 (2), 195-202. e9, 2021 | 37 | 2021 |
| Learning compositional representations of interacting systems with restricted boltzmann machines: Comparative study of lattice proteins J Tubiana, S Cocco, R Monasson Neural computation 31 (8), 1671-1717, 2019 | 36 | 2019 |
| Statistical physics and representations in real and artificial neural networks S Cocco, R Monasson, L Posani, S Rosay, J Tubiana Physica A: Statistical Mechanics and its Applications 504, 45-76, 2018 | 26 | 2018 |
| Blind deconvolution for spike inference from fluorescence recordings J Tubiana, S Wolf, T Panier, G Debregeas Journal of neuroscience methods 342, 108763, 2020 | 19* | 2020 |
| Inference of compressed Potts graphical models F Rizzato, A Coucke, E de Leonardis, JP Barton, J Tubiana, R Monasson, ... Physical Review E 101 (1), 012309, 2020 | 19 | 2020 |
| ‘Place-cell’emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space M Harsh, J Tubiana, S Cocco, R Monasson Journal of Physics A: Mathematical and Theoretical 53 (17), 174002, 2020 | 17 | 2020 |
| Superimmunity by pan-sarbecovirus nanobodies Y Xiang, W Huang, H Liu, Z Sang, S Nambulli, J Tubiana, KL Williams, ... Cell Reports 39 (13), 2022 | 16* | 2022 |
| Restricted Boltzmann machines: from compositional representations to protein sequence analysis J Tubiana Université Paris sciences et lettres, 2018 | 16 | 2018 |
| Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity TL van der Plas, J Tubiana, G Le Goc, G Migault, M Kunst, H Baier, ... eLife 12, e83139, 2023 | 15* | 2023 |
| Reduced B cell antigenicity of Omicron lowers host serologic response J Tubiana, Y Xiang, L Fan, HJ Wolfson, K Chen, D Schneidman-Duhovny, ... Cell reports 41 (3), 2022 | 10 | 2022 |
| Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies C Malbranke, D Bikard, S Cocco, R Monasson, J Tubiana Current Opinion in Structural Biology 80, 102571, 2023 | 9* | 2023 |
| Scannet: A web server for structure-based prediction of protein binding sites with geometric deep learning J Tubiana, D Schneidman-Duhovny, HJ Wolfson Journal of Molecular Biology 434 (19), 167758, 2022 | 7 | 2022 |
| Discriminating physiological from non‐physiological interfaces in structures of protein complexes: A community‐wide study H Schweke, Q Xu, G Tauriello, L Pantolini, T Schwede, F Cazals, ... Proteomics 23 (17), 2200323, 2023 | 5 | 2023 |
| Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions J Tubiana, L Adriana-Lifshits, M Nissan, M Gabay, I Sher, M Sova, ... PLoS computational biology 19 (2), e1010874, 2023 | 4 | 2023 |
| EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning N Nagar, J Tubiana, G Loewenthal, HJ Wolfson, NB Tal, T Pupko Journal of Molecular Biology 435 (14), 168155, 2023 | 1 | 2023 |
