bioRxiv 2025.10.26.684604 <\a>
[116]
Design and experimental characterization of specificity-switching mutational paths of WW domains
bioRxiv 2025.12.08.693000; <\a>
[115]
PRIX NOBEL DE PHYSIQUE 2024
[114]
Speed Vascular Patterns in the Spatial Navigation System
[113]
Restoring data balance via generative models of T cell receptors for antigen-binding prediction
[112]
Linking Brain and Behavior States in Zebrafish Larvae Locomotion using Hidden Markov Models
[111]
Generative model of SARS-CoV-2 variants under functional and immune pressure unveils viral escape potential and antibody resilience
[110] Learning with artificial and natural neural networks: trade-offs in energy consumption and representations
[109] Task learning through stimulation-induced plasticity in neural networks
[108] Unlearning regularization for Boltzmann Machines
[107]
Replica symmetry breaking and clustering phase transitions in undersampled restricted Boltzmann machines
[106] Deciphering the code of viral-host adaptation through maximum entropy model
[105] Designing Molecular RNA Switches with Restricted Boltzmann Machines
[104] Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information
[103] Restoring balance: principled under/oversampling for optimal data classification
[102] Stimulation allows for reshaping network connectivity through
plasticity: a training protocol for rate models
[101] Transition paths in Potts-like energy landscapes: general properties and application to protein sequence models
[100] Evolutionary Dynamics of a Lattice Dimer: a Toy Model for Stability vs. Affinity Trade-offs in Proteins
[99] Accelerated Sampling with Stacked Restricted Boltzmann Machines
[98]Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment
[97] Infer global, predict local: quantity-relevance trade-off in protein fitness predictions from sequence data
[96] Learning the differences: a transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.
[95] Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies
[94] Statistical-physics approaches to RNA molecules, families and networks.
[93] Disentangling representations in Restricted Boltzmann Machines without adversaries.
[92] Mutational paths in protein-sequence landscapes: from sampling to mean-field characterization
[91] Emergence of time persistence in an interpretable data-driven neural network model.
S. Wolf, G. Le Goc, G. Debregeas, S.Cocco, R.Monasson.
[90] Optimal regularizations for data generation with probabilistic graphical models.
[89] Displacement and dissociation of oligonucleotides during DNA hairpin closure under strain
[88] Neoantigen Quality predicts immunoediting in survivors of pancreatic cancer
[87] Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.
[86] Origins and breadth of pairwise epistasis in an alpha-helix of
beta-lactamase TEM-1
[85] sgDI-tector: defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs.
[84] Repeats Mimic Immunostimulatory Viral Features Across a Vast Evolutionary Landscape.
[83] Barriers and dynamical paths in alternating Gibbs sampling of restricted Boltzmann machines
[82] Inferring epistasis from genomic data with comparable mutation and outcrossing rate
[81] A synaptic novelty signal in the dentate gyrus supports switching hippocampal attractor networks from generalization to discrimination
[80] Probing T-cell response by sequence-based probabilistic modeling
[79] Survival probability and size of lineages in antibody affinity maturation
[78] Improving sequence-based modeling of protein families using secondary structure quality assessment
[77] Gaussian Closure Scheme in the Quasi-Linkage Equilibrium Regime of Evolving Genome Populations
[76] The heterogeneous landscape and early evolution of pathogen-associated CpG dinucleotides in SARS-CoV-2
[75] RBM-MHC: a semi-supervised machine-learning method for sample-specific prediction of antigen presentation by HLA-I alleles
[74] An evolution-based model for designing chorismate mutase enzymes
[73] Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers
[72] 'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
[71] Parameters and determinants of responses to selection in antibody libraries
[70] Inference of compressed Potts graphical models
[69] Adaptive cluster expansion for Ising spin models
[68] Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
[67] Learning protein constitutive motifs from sequence data
[66] Adaptation of olfactory receptor abundances for efficient coding
[65] Integration and multiplexing of positional and contextual information by the hippocampal network
[64] Statistical Physics and Representations in Real and Artificial Neural Networks
[63] Inverse Statistical Physics of Protein Sequences: A Key Issues Review
[62] Evolutionary constraints on coding sequences at the nucleotidic level: a statistical physics approach
[61] Functional Networks from Inverse Modeling of Neural Population Activity
[60] Functional connectivity models for brain state identification: application to decoding of spatial representations from hippocampal CA1 and CA3 recordings
[59] Direct coevolutionary couplings reflect biophysical residue interactions in proteins
[58] Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings
[57] ACE: adaptive cluster expansion for maximum entropy graphical model inference
[56] Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models
[55] Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity
[54] On the entropy of protein families
[53] Distinguishing the Immunostimulatory Properties of Non-coding RNAs Expressed in Cancer Cells
[52] Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction
[51] Learning probability distributions from smooth observables and the maximum entropy principle: some remarks
[50] Large Pseudo-Counts and L2-Norm Penalties Are Necessary for the Mean-Field Inference of Ising and Potts Models
[49] Stochastic Ratchet Mechanisms for Replacement of Proteins Bound to DNA
[48] A Quantitative Theory of Entropic Forces Acting on Constrained Nucleotide Sequences Applied to Viruses
[46] Trend or Fluctuations? Analysis and design of population dynamics measurements in replicate ecosystems.
[45] Hopfield-Potts patterns for covariation in protein families: calculation and statistical error bars
[44] From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction
[43] Ising models for neural activity inferred via Selective Cluster Expansion: structural and coding properties.
[42] Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests
[41] High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections.
[40] On the trajectories and performance of Infotaxis, an information-based greedy search algorithm.
[39] Adaptive cluster expansion for inferring Boltzmann machines with noisy data.
[38] Fast Inference of Interactions in Assemblies of Stochastic Integrate-and-Fire Neurons from Spike Recordings
[37] Inference of a random potential from random walk realizations: formalism and application to the one-dimensional Sinai model with a drift
[36] Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods.
[35] Dynamical modelling of molecular constructions and setups for DNA unzipping.
[34] Reconstructing a random potential from its random walks.
[33] Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case.
[32] The mechanical opening of DNA and the sequence content
[31] Inference of DNA sequences from mechanical unzipping experiments: an ideal-case study
[30] Comment on the paper: Comparison of the measured phase diagrams in the force-temperature plane for the unzipping of two different natural DNA sequences, by C.H. Lee, C. Danilowicz, V.W. Coljee, and M. Prentiss.
[Edited book] Multiple aspects of DNA and RNA: from biophysics to bioinformatics.
[29] Loops in DNA: an overview of experimental and theoretical approaches.
[28] Protein-mediated DNA loops: effects of protein bridge size and kinks.
[27] Dynamics of excitatory synaptic components in sustained firing at low rates.
[27a] Role of calcium and noise in the persistent activity of an isolated neuron
[26] Overstretching and force-driven strand separation of double-helix DNA
[25] Analysis of backtracking procedures for random decision problems
[24] Approximate analysis of search algorithms with ``physical'' methods.
[23] The micromechanics of DNA
[22] Rigorous decimation-based construction of ground pure states for spin glass models on random lattices.
[21] Force-extension behavior of folding polymers.
[20] Unzipping dynamics of long DNAs.
[19] Theoretical models for single-molecule DNA and RNA experiments: from elasticity to unzipping.
[18] Slow nucleic acid unzipping kinetics from sequence-defined barriers.
[17] Exponentially hard problems are sometimes polynomial, a large deviation analysis of search algorithms for the random Satisfiability problem, and its application to stop-and-restart resolutions.
[16]Heuristic average-case analysis of the backtrack resolution of random 3-Satisfiability instances.
[15] Restarts and exponential acceleration of random 3-SAT instances resolutions: a large deviation analysis of the Davis-Putnam-Loveland-Logemann algorithm.
[14] Phase transitions and Complexity in computer science: An overview of the statistical physics approach to the random satisfiability problem.
[13] A la rescousse de la complexité calculatoire.
[12] Force and kinetic barriers to initiation of DNA unzipping.
[11] Force and kinetic barriers in unzipping of DNA.
[10] Statistical physics analysis of the computational complexity of solving random satisfiability problems using branch and bound search algorithms.
[9] Le temps d'un choix : transitions de phase et complexité en informatique.
[8] Trajectories in phase diagrams, growth processes and computational complexity: how search algorithms solve the 3-Satisfiability problem.
A. Rehan, E. Mauri, J. Fernandez-de-Cossio-Diaz, Pierre-Guillaume Brun, R.Monasson,M.Ribezzi,S. Cocco
Simona Coco, R.Monasson
Universalis 2025
Full version
F C Pereira, S H. Castedo, S Le Meur-Diebolt, N Ialy-Radio, S Bhattacharya, J Ferrier, B F Osmanski, S Cocco, R Monasson, S Pezet, M. Tanter
bioRXiv 2025.07.15.663536 doi https://doi.org/10.1101/2025.07.15.663536 accepted in Cell Reports
Emanuele Loffredo, Mauro Pastore, Simona Cocco, Rémi Monasson
bioRXiv 2024.07.10.602897 doi: https://doi.org/10.1101/2024.07.10.602897
M. Dommanget-Kott, J. Fernandez-de-Cossio-Diaz, M. Coraggioso, V. Bormuth, R. Monasson, G. Debrégeas, S. Cocco
bioRxiv 2024.11.22.624881; doi: https://doi.org/10.1101/2024.11.22.624881
accepted in Plos Comp Bio
M. Huot,P. Rosenbaum,C. Planchais,H. Mouquet,R. Monasson, S. Cocco,
doi: https://doi.org/10.1101/2025.05.12.653592
S. Cocco,R.Monasson
F. Borra, S. Cocco, R. Monasson
Physical Review X Life (2024) 2, 043014
DOI: https://doi.org/10.1103/PRXLife.2.043014
E. Ventura, S. Cocco, R. Monasson, F. Zamponi
Machine Learning: Science and Technology 5, 025078 (2024)
Jorge Fernandez-De-Cossio-Diaz ,Thomas Tulinski, Simona Cocco, R. Monasson
hal-04447899v1 (2024);
A. Di Gioacchino, I Lecce, B.D. Greenbaum, R. Monasson, S. Cocco
Molecular Biology and Evolution, 42, 6 (2025);
J. Fernandez-de-Cossio-Diaz, P. Hardouin, F-X. Lyonnet du Moutier, A. Di Gioacchino, B. Marchand, Y. Ponty, B. Sargueil, R. Monasson, S. Cocco
Nature Communication 16: 11223 (2025)
S. Cocco, L. Posani, R. Monasson.
PNAS doi: 10.1073/pnas.2312335121 (2024)
Supplementary Material
E. Loffredo, M. Pastore, S. Cocco, R. Monasson
Forty-first International Conference on Machine Learning - ICML (2024)
F. Borra, S. Cocco, R. Monasson
Computational and Systems Neuroscience - COSYNE 2024 (2024)
E. Mauri, S. Cocco, R. Monasson
Phys. Rev. E 108, 024141 (2023)
E. Loffredo, E. Vesconi, R. Razban, O. Peleg, E. Shakhnovich, S. Cocco, and R. Monasson
J. Phys. A 56 455002 (2023); Special issue on Random Landscapes and
Dynamics in Evolution, Ecology and Beyond
J. Fernandez-De-Cossio-Diaz, C. Roussel, S. Cocco, R. Monasson.
Twelth Conference on International Conference on Learning
Representations - ICLR (2024)
C. Malbranke, W. Rostain, F. Depardieu, S. Cocco, R. Monasson, D. Bikard.
PLoS Computational Biology 19:e1011621 (2023)
L. Posani, F. Rizzato, R. Monasson, S. Cocco
PLoS Computational Biology 19:e1011521 (2023)
B. Bravi, A. Di Gioacchino, J. Fernandez-de-Cossio-Diaz, A. M. Walczak, T. Mora, S. Cocco, and R. Monasson.
.
eLife 12:e85126 (2023)
C. Malbranke, D. Bikard, S. Cocco, R. Monasson, J. Tubiana
Current Opinion in Structural Biology 80:102571 (2023)
S. Cocco, A. De Martino, A. Pagnani, M. Weigt.
.Spin Glass Theory and Far Beyond - Replica Symmetry Breaking after 40 years" (edited by P Charbonneau, E Marinari, G Parisi, F Ricci Tersenghi, G Sicuro and F Zamponi)
J. Fernandez-de-Cossio-Diaz, S. Cocco, R. Monasson
Physical Review X 13, 021003 (2023)
E. Mauri, S. Cocco, R. Monasson
Physical Review Letters 130, 158402 (2023)
eLife 12:e79541 (2023)
A. Fanthomme, F. Rizzato, S. Cocco, R. Monasson.
.J. Stat. Mech. 053502 (2022)
F. Ding, S. Cocco, S. Raj, M. Manosas, M. M. Spiering, D. Bensimon, J-F. Allemand, V. Croquette.
Nucleic Acids Res,50(21):12082(2022).
M. Luksa, M. Sethna,.., S. Cocco, C. Iacobuzio-Donahue, B. Greenbaum, V.P. Balachndran
Nature 606, 389 (2022)
A. Di Gioacchino, J. Procyk, M. Molari, J. S. Schreck, Y. Zhou, Y. Liu, R. Monasson, S. Cocco, P. Šulc.
Plos Computational Biology,18(9): e1010561 (2022)
A. Birgy, C. Roussel, H. Kemble, J. Mullaert, K.
Panigoni, A. Chapron, J. Chatel, M. Magnan, H.
Jacquier, S. Cocco, R. Monasson, O. Tenaillon
submitted (2023)
A. Di Gioacchino, R. Legendre, Y. Rahou, V. Najburg, P. Charneau, B. D Greenbaum, F. Tangy, S. van der Werf, S. Cocco, A. V Komarova.
RNA 078969.121 (2021)
P. Sulc, A. Solovyov,S. Marhou. S. Sun, JA . LaCava, O I Abdel-Wahab, N. Vabret, D. De Carvalho, R. Monasson, S. Cocco, B. D. Greenbaum.
Cell Genomics 5, 101011 (2025)
C. Roussel, S. Cocco, R. Monasson
Phys. Rev. E 104,034109 ( 2021)
H-L. Zeng, E. Mauri, V. Dichio, S. Cocco, R. Monasson, E. Aurell
J. Stat. Mech. 083501 (2021)
R. Gomez-Ocadiz, M. Trippa, L. Posani, S. Cocco, R. Monasson, C. Schmidt-Hieber
Nature Communications 13, 4122 (2022)
B. Bravi, V.P. Balachandran, B.D. Greenbaum, A.M. Walczak, T. Mora, R. Monasson, S. Cocco
PLoS Comp Bio 17 (9): e1009297 (2021)
M. Molari, R. Monasson, S. Cocco
Physical Review E 103, 052413 (2021)
C. Malbranke, D. Bikard, S. Cocco, R. Monasson
Bioinformatics, btab442 (2021)
E. Mauri, S. Cocco, R. Monasson
Europhysics Letters 132, 56001 (2020)
A. Di Gioacchino, P. Sulc, A.V. Komarova, B.D. Greenbaum, R. Monasson, S. Cocco
Molecular Biology and Evolution 38, 2428-2445 (2021)
B. Bravi, J. Tubiana, S. Cocco, R. Monasson, T. Mora, A.M. Walczak
Cell Systems 12, 1-8 (2021)
W.P. Russ, M. Figliuzzi, C. Stocker, P. Barrat-Charlaix, M. Socolich, P. Kast, D. Hilvert, R. Monasson, S. Cocco, M. Weigt, R. Ranganathan
Science 369, 440-5 (2020)
M. Molari, K. Eyer, J. Baudry, S. Cocco, R. Monasson
eLife 2020 9:e55678 (2020)
M. Harsh, J. Tubiana, S. Cocco, R. Monasson
J. Phys. A 53, 174002 (2020)
S. Schulz, S. Boyer, M. Smerlak, S. Cocco, R. Monasson, C. Nizak, O. Rivoire
PLoS Comp. Biol. 17:e1008751 (2021)
F. Rizzato, A. Coucke, E. de Leonardis, J.P. Barton, J. Tubiana, R. Monasson, S. Cocco
Physical Review E 101, 012309 (2020)
S. Cocco; G. Croce, F. Zamponi
The European Physical Journal B DOI 10.1140/EPJB/E2019-100313-9,2019
J. Tubiana, S. Cocco, R. Monasson
Neural Computation 31(8), 1671-1717 (2019)
J. Tubiana, S. Cocco, R. Monasson
eLife 2019;8:e39397 (2019). See also the press release.
T. Tesileanu, S. Cocco, R. Monasson, V. Balasubramanian
eLife 2019;8:e39279 (2019). See also the press release.
L. Posani, S. Cocco, R. Monasson
PLoS Computational Biology 14: e1006320 (2018).
S. Cocco, R. Monasson, L. Posani, S. Rosay, J. Tubiana
Lectures Notes of Fundamental Problems in Statistical Physics XIV, Physica A 504, 45-76 (2018)
S. Cocco, C. Feinauer, M. Figliuzzi, R. Monasson, M. Weigt
Reports on Progress in Physics 81, 032601 (2018).
D. Chatenay, S. Cocco, B. Greenbaum, R. Monasson, P. Netter
chapter of "Evolutionary Biology: Self/Nonself Evolution, Species and Complex Traits Evolution, Methods and Concepts", Editor P. Pontarotti (2017).
S. Cocco, R. Monasson, L. Posani, G. Tavoni
Current Opinion in Systems Biology 3, 103-110 (2017)
L. Posani, S. Cocco, K. Jezek, R. Monasson
J. Comp. Neurosci. 43, 17-33 (2017).
A. Coucke, G. Uguzzoni, F. Oteri, S. Cocco, R. Monasson, M. Weigt
J. Chem. Phys. 145, 174102 (2016).
G. Tavoni, S. Cocco, R. Monasson
J. Comp. Neurosci. 41, 269-293 (2016).
JP Barton, E. De Leonardis, A. Coucke, S. Cocco
Bioinformatics doi: 10.1093/bioinformatics/btw328 (2016)
H. Jacquin, A. Gilson, E. Shakhnovich, S. Cocco, R. Monasson
PLoS Comput Biol 12: e1004889 (2016)
G. Tavoni, U. Ferrari, F.P. Battaglia, S. Cocco, R. Monasson
Network Neuroscience 1, 275-301 (2017)
(supporting information)
J.P. Barton, A.K. Chakraborty, S. Cocco, H. Jacquin, R. Monasson
Journal of Statistical Physics 162, 1267-1293 (2016)
A. Tanne, L. Muniz, A. Puzio-Kuter, K. Leonova, A. Gudkov, D. Ting, R. Monasson, S. Cocco, A. Levine, N. Bhardwaj, B. Greenbaum
Proc. Natl. Acad. Sci. USA 112, 15154-15159 (2015), doi: 10.1073/pnas.1517584112 (supplementary methods and experiments),
see also Immunostimulatory noncoding RNAs, in Highlights (Medical Sciences)
and the commentary Silent pericentromeric repeats speak out by S.T. Younger and J.L. Rinn.
E. De Leonardis, S. Lutz, S. Ratz, S. Cocco, R. Monasson, A. Schug, M. Weigt
Nucleic Acid Research, doi: 10.1093/nar/gkv932 (2015) (supplemental text and supplemental figures)
T. Obuchi, R. Monasson
Journal of Physics Conf. Ser. 638, 012018 (2015)
J.P.Barton, S. Cocco, E. De Leonardis, R. Monasson
Physical Review E 90, 012132 (2014)
S. Cocco, J.F. Marko, R. Monasson
Physical Review Letters 112, 238101 (2014) (supplemental material)
B. Greenbaum, S. Cocco, A. Levine, R. Monasson
Proc. Natl. Acad. Sci. USA 111, 5054-5059 (2014)
C. Barbieri, S. Cocco, T. Jorg, R. Monasson
Biophysical Journal 106, 430-9 (2014)(supporting material)
D.R. Hekstra, S. Cocco, R. Monasson, S. Leibler
Physical Review E 88, 062714 (2013)
(supplementary information
S. Cocco, R. Monasson, M. Weigt
J. Phys. Conference Series 473, 012010 (2013)
S. Cocco, R. Monasson, M. Weigt
PLoS Comput Biol 9, E1003176 (2013) ((supplementary information)
J.P Barton, S. Cocco
Journal of Statistical Mechanics: Theory and Experiment.P03002 (2013)
S. Cocco, R. Monasson
J. Stat. Phys. 147, 252 (2012) <
S. Cocco, R. Monasson, V. Sessak
Physical Review E 83, 051123 (2011)
C. Barbieri, S. Cocco, R. Monasson
Europhysics Letters 94, 20005 (2011)
S. Cocco, R. Monasson
Physical Review Letters 106, 090601 (2011) (supplementary information)
R. Monasson, S. Cocco
Journal of Computational Neuroscience 31, 199-227 (2011)
S. Cocco, R. Monasson
Journal of Physics: Conference Series 197, 012005 (2009)
S. Cocco, S. Leibler, R. Monasson
Proc. Natl. Acad. Sci. USA 106, 14058 (2009) (supplementary information)
C. Barbieri, S. Cocco, R. Monasson, F. Zamponi
Phys. Biol. 6, 025003 (2009)
S. Cocco, R. Monasson.
Europhysics Letters 81, 20002 (2008)
V. Baldazzi, S. Bradde, S. Cocco, E. Marinari, R. Monasson
Phys. Rev. E 75, 011904 (2007).
S. Cocco, R. Monasson
AIP Conference Proceedings, vol 851, p 50 (2006)
V. Baldazzi, S. Cocco, E. Marinari, R. Monasson
Phys. Rev. Lett. 96, 128102 (2006).
S.Cocco
Eur Phys J E 19, 345 (2006).
D. Chatenay, S. Cocco, R. Monasson, D. Thieffry, J. Dalibard (eds)
Lecture Notes of Les Houches Summer School, Elsevier (2005)
J.F. Allemand, S. Cocco , N. Douarche N, G. Lia
Eur Phys J E 19, 293 (2006).
N. Douarche, S. Cocco
Phys Rev E 72, 061902 (2005).
C. Wyart, S. Cocco, L. Bourdieu, J. F. Leger, C. Herr , D. Chatenay
J Neurophysiology 93, 3370 (2005).
S.Cocco
arXiv:q-bio/0406010
S. Cocco, Y. Yan, J-F. Leger, D. Chatenay, J.F. Marko
Phys Rev E 70, 011910 (2004).
S. Cocco, L. Ein-Dor, R. Monasson.
Chapter for "New optimization algorithms in physics" edited by A. Hartmann, H. Rieger, Wiley (2004)
S. Cocco, R. Monasson, A. Montanari, G. Semerjian.
Chapter for "Phase transitions and Algorithmic complexity" edited by G. Istrate, C. Moore, A. Percus (2004)
S. Cocco, J.F. Marko
Physics World 16, 37 (2003).
S. Cocco, O. Dubois, J. Mandler, R. Monasson.
Phys. Rev. Lett. 90, 047205 (2003)
S. Cocco, J.F. Marko, R. Monasson, A. Sarkar, J. Ya.
Eur. Phys. J. E 10, 249 (2003).
S. Cocco, R. Monasson, J.F. Marko.
Phys. Rev. E 66, 051914 (2002).
S. Cocco, J.F. Marko, R. Monasson.
C.R. Physique 3, 569-584 (2002)
S. Cocco, R. Monasson, J.F. Marko.
Eur. Phys. J. E 10, 153 (2003).
S. Cocco, R. Monasson.
Phys. Rev. E 66, 037101 (2002)
S. Cocco, R. Monasson.
Theoretical Computer Science A 320, 345 (2004).
S. Cocco, R. Monasson.
Annals of Mathematics and Artificial Intelligence 43, 153-172 (2005).
G. Biroli, S. Cocco, R. Monasson.
Physica A 306, 381-394 (2002).
S. Cocco, O. Dubois, J. Mandler, R. Monasson.
Pour la Science, Mai 2002, Editions Belin.
S. Cocco, R. Monasson, J. Marko.
Phys. Rev. E 65, 041907 (2002).
S. Cocco, R. Monasson, J. Marko.
Proc. Natl. Acad. Sci. USA 98, 8608 (2001).
S. Cocco, R. Monasson.
Eur. Phys. J. B 22, 505 (2001).
G. Biroli, S. Cocco, R. Monasson.
Images de la Physique 2001, CNRS Editions.
S. Cocco, R. Monasson.
Phys. Rev. Lett. 86, 1654 (2001).
[6] Statistical Mechanics of Torque Induced Denaturation of DNA.
S. Cocco, R. Monasson.
Phys. Rev. Lett. 83, 5178 (1999)
[5] A twist opening model for DNA.
M. Barbi, S. Cocco, M. Peyrard, S. Ruffo.
Journal of Biological Physics 24, 97 (1999).
[4] Helicoidal model for DNA opening.
[3] S. Cocco, M. Barbi, M. Peyrard.
[2] S. Cocco, R. Monasson, R. Zecchina.
[1] Analytical and numerical study of internal representations in multilayer neural networks with binary weights.
M. Barbi, S. Cocco, M. Peyrard.
Physics Letters A 253, 358 (1999).
Vector Nonlinear Klein-Gordon Lattices: General Derivation of Small Amplitude Envelope Soliton Solutions.
Physics Letters A 253, 161-167 (1999).
The weight space structure of the parity machine with binary weights: analytical and numerical results.
Proceedings of the Varenna School of physics Enrico Fermi course CXXXIV, edited by F. Mallamace and H. E. Stanley (IOS Press, Amsterdam) 738-739 (1997).
S. Cocco, R. Monasson, R. Zecchina.
Phys. Rev. E 54, 717 (1996).