Emergence of Compositional Representations in Restricted Boltzmann Machines
J. Tubiana, R. Monasson, "Emergence of Compositional Representations in Restricted Boltzmann Machines." Physical Review Letters, 2017.
CV
Education
- B.S. in GitHub, GitHub University, 2012
- M.S. in Jekyll, GitHub University, 2014
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
Work experience
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Learning protein constitutive motifs from sequence data
Jérôme Tubiana, Simona Cocco, Rémi Monasson, "Learning protein constitutive motifs from sequence data." eLife, 2019.
Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
Jérôme Tubiana, Simona Cocco, Rémi Monasson, "Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins." Neural Computation, 2019.
Inference of compressed Potts graphical models
Francesca Rizzato, Alice Coucke, Eleonora Leonardis, John Barton, Jérôme Tubiana, Rémi Monasson, Simona Cocco, "Inference of compressed Potts graphical models." Physical Review E, 2020.
‘Place-cell’ emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
Moshir Harsh, Jérôme Tubiana, Simona Cocco, Rémi Monasson, "‘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.
Blind deconvolution for spike inference from fluorescence recordings
Jérôme Tubiana, Sébastien Wolf, Thomas Panier, Georges Debregeas, "Blind deconvolution for spike inference from fluorescence recordings." Journal of Neuroscience Methods, 2020.
RBM-MHC: A Semi-Supervised Machine-Learning Method for Sample-Specific Prediction of Antigen Presentation by HLA-I Alleles
Barbara Bravi, Jérôme Tubiana, Simona Cocco, Rémi Monasson, Thierry Mora, Aleksandra Walczak, "RBM-MHC: A Semi-Supervised Machine-Learning Method for Sample-Specific Prediction of Antigen Presentation by HLA-I Alleles." Cell Systems, 2021.
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
Jérôme Tubiana, Dina Schneidman-Duhovny, Haim Wolfson, "ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction." Nature Methods, 2022.
Superimmunity by pan-sarbecovirus nanobodies
Yufei Xiang, Wei Huang, Hejun Liu, Zhe Sang, Sham Nambulli, Jérôme Tubiana, Kevin Williams, W. Duprex, Dina Schneidman-Duhovny, Ian Wilson, Derek Taylor, Yi Shi, "Superimmunity by pan-sarbecovirus nanobodies." Cell Reports, 2022.
Reduced B-cell antigenicity of Omicron lowers host serologic response
Jérôme Tubiana, Yufei Xiang, Li Fan, Haim Wolfson, Kong Chen, Dina Schneidman-Duhovny, Yi Shi, "Reduced B-cell antigenicity of Omicron lowers host serologic response." Cell Reports, 2022.
ScanNet: A Web Server for Structure-based Prediction of Protein Binding Sites with Geometric Deep Learning
Jérôme Tubiana, Dina Schneidman-Duhovny, Haim Wolfson, "ScanNet: A Web Server for Structure-based Prediction of Protein Binding Sites with Geometric Deep Learning." Journal of Molecular Biology, 2022.
Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity
Thijs Plas, Jérôme Tubiana, Guillaume Le, Geoffrey Migault, Michael Kunst, Herwig Baier, Volker Bormuth, Bernhard Englitz, Georges Debrégeas, "Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity." eLife, 2023.
Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions
Jérôme Tubiana, Lucia Adriana-Lifshits, Michael Nissan, Matan Gabay, Inbal Sher, Marina Sova, Haim Wolfson, Maayan Gal, "Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions." PLOS Computational Biology, 2023.
Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies
Cyril Malbranke, David Bikard, Simona Cocco, Rémi Monasson, Jérôme Tubiana, "Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies." Current Opinion in Structural Biology, 2023.
EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning
Natan Nagar, Jérôme Tubiana, Gil Loewenthal, Haim Wolfson, Nir Ben, Tal Pupko, "EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning." Journal of Molecular Biology, 2023.
Talks
Talk 1 on Relevant Topic in Your Field
Talk at UC San Francisco, Department of Testing, San Francisco, California
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA
Teaching
Service and leadership
- Currently signed in to 43 different slack teams