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Autor:
Alex Hawkins-Hooker, Arthur Chen, David Bikard, Guillaume Couairon, Florence Depardieu, Sebastien Baur
Publikováno v:
PLoS Computational Biology
PLoS Computational Biology, 2021, 17 (2), pp.e1008736. ⟨10.1371/journal.pcbi.1008736⟩
PLoS Computational Biology, Public Library of Science, 2021, 17 (2), pp.e1008736. ⟨10.1371/journal.pcbi.1008736⟩
PLoS Computational Biology, Vol 17, Iss 2, p e1008736 (2021)
PLoS Computational Biology, 2021, 17 (2), pp.e1008736. ⟨10.1371/journal.pcbi.1008736⟩
PLoS Computational Biology, Public Library of Science, 2021, 17 (2), pp.e1008736. ⟨10.1371/journal.pcbi.1008736⟩
PLoS Computational Biology, Vol 17, Iss 2, p e1008736 (2021)
The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5955b622447ca55f7098b466df9b8783
https://hal-pasteur.archives-ouvertes.fr/pasteur-03263932/document
https://hal-pasteur.archives-ouvertes.fr/pasteur-03263932/document