Martin Weigt
researcher
Facts
8 properties- Instance of
- human
- Date of birth
- 1970
- Occupation
- researcher, physicist
- Doctoral student
- Giancarlo Croce[6]
- Maintained by WikiProject
- WikiProject Mathematics[8]
🏷️ Also known as
en Martin Weigt
🔖 Identifiers · 19
- ISNI
- 0000000116325158
- ORCID iD
- 0000-0002-0492-3684
- VIAF cluster ID
- 45134982
- Library of Congress authority ID
- no2005104791
- GND ID
- 12054539X
- DDB person (GND) ID
- 12054539X
- Dimensions author ID
- 0625323361.32
- HAL author ID
- martin-weigt
- IdRef ID
- 170048632
- NUKAT ID
- n2010155865
- National Library of Israel J9U ID
- 987007317379005171
- Publons author ID
- 2229195
Show all 19 identifiers +7
- ResearcherID
- L-8851-2016
- Savoirs ENS ID
- 2332
- Scopus author ID
- 57200771646
- Springer Nature person ID
- 0625323361.32
- WorldCat Entities ID
- E39PBJghvc6WwtgjtHT4FD9hpP
- Yale LUX ID
- person/63f37009-2e35-4a0b-946b-8fe9f66acd68
- ZbMATH author ID
- weigt.martin
🔗 Connections
Author of 21
adabmDCA. Adaptive Boltzmann machine learning for biological sequences,
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1,
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins,
Direct coevolutionary couplings reflect biophysical residue interactions in proteins,
Efficient generative modeling of protein sequences using simple autoregressive models,
Emergent time scales of epistasis in protein evolution,
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes,
FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution
+13 more
← Authored by 21
adabmDCA. Adaptive Boltzmann machine learning for biological sequences,
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1,
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins,
Direct coevolutionary couplings reflect biophysical residue interactions in proteins,
Efficient generative modeling of protein sequences using simple autoregressive models,
Emergent time scales of epistasis in protein evolution,
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes,
FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution
+13 more
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