Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Martin Weigt

Showing results (21-30 of 64) with videos related to

Pageof 7
Sort By:
BMC Bioinformatics|October 30, 2021
adabmDCA: adaptive Boltzmann machine learning for biological sequencesAnna Paola Muntoni, Andrea Pagnani, Martin Weigt, et al.
Physical Review Letters|October 26, 2005
Cavity approach to the random solid stateXiaoming Mao, Paul M Goldbart, Marc Mézard, et al.
Molecular Biology and Evolution|November 9, 2021
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein EvolutionMatteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, et al.
Reports on Progress in Physics. Physical Society (Great Britain)|July 1, 2025
Fluctuations and the limit of predictability in protein evolutionSaverio Rossi, Leonardo Di Bari, Martin Weigt, et al.
Proceedings of the National Academy of Sciences of the United States of America|October 13, 2016
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysisThomas Gueudré, Carlo Baldassi, Marco Zamparo, et al.
Nature Communications|October 5, 2021
Efficient generative modeling of protein sequences using simple autoregressive modelsJeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, et al.
Nucleic Acids Research|September 3, 2025
Integrating experimental feedback improves generative models for biological sequencesFrancesco Calvanese, Giovanni Peinetti, Polina Pavlinova, et al.
Molecular Biology and Evolution|October 9, 2015
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1Matteo Figliuzzi, Hervé Jacquier, Alexander Schug, et al.
Reports on Progress in Physics. Physical Society (Great Britain)|November 10, 2017
Inverse statistical physics of protein sequences: a key issues reviewSimona Cocco, Christoph Feinauer, Matteo Figliuzzi, et al.
Nature Communications|April 2, 2022
Author Correction: Efficient generative modeling of protein sequences using simple autoregressive modelsJeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, et al.
Pageof 7

Showing results (21-30 of 64) with videos related to

Sort By:
Pageof 7
BMC Bioinformatics|October 30, 2021
adabmDCA: adaptive Boltzmann machine learning for biological sequencesAnna Paola Muntoni, Andrea Pagnani, Martin Weigt, et al.
Physical Review Letters|October 26, 2005
Cavity approach to the random solid stateXiaoming Mao, Paul M Goldbart, Marc Mézard, et al.
Molecular Biology and Evolution|November 9, 2021
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein EvolutionMatteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, et al.
Reports on Progress in Physics. Physical Society (Great Britain)|July 1, 2025
Fluctuations and the limit of predictability in protein evolutionSaverio Rossi, Leonardo Di Bari, Martin Weigt, et al.
Proceedings of the National Academy of Sciences of the United States of America|October 13, 2016
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysisThomas Gueudré, Carlo Baldassi, Marco Zamparo, et al.
Nature Communications|October 5, 2021
Efficient generative modeling of protein sequences using simple autoregressive modelsJeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, et al.
Nucleic Acids Research|September 3, 2025
Integrating experimental feedback improves generative models for biological sequencesFrancesco Calvanese, Giovanni Peinetti, Polina Pavlinova, et al.
Molecular Biology and Evolution|October 9, 2015
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1Matteo Figliuzzi, Hervé Jacquier, Alexander Schug, et al.
Reports on Progress in Physics. Physical Society (Great Britain)|November 10, 2017
Inverse statistical physics of protein sequences: a key issues reviewSimona Cocco, Christoph Feinauer, Matteo Figliuzzi, et al.
Nature Communications|April 2, 2022
Author Correction: Efficient generative modeling of protein sequences using simple autoregressive modelsJeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, et al.
Pageof 7