Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Max Schwendemann"'
Autor:
Daniel Heid, Dominik Niopek, Max C. Waldhauer, Irina Lehmann, Moritz Jakob Przybilla, Pauline L. Pfuderer, Roland Eils, Thore Bürgel, Catharina Gandor, Marita Klein, Mareike D. Hoffmann, Carolin Schmelas, Felix Bubeck, Julius Upmeier zu Belzen, Lukas Platz, Jan Mathony, Lukas Adam, Stefan Holderbach, Max Schwendemann, Michael Jendrusch
Publikováno v:
Nature Machine Intelligence. 1:225-235
Proteins are nature’s most versatile molecular machines. Deep neural networks trained on large protein datasets have recently been used to tackle the unmet complexity of protein sequence–function relationships. The implicit knowledge contained in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10e606d599d7c8b4e6ebd1de2fc5b7df
https://doi.org/10.1038/s42256-019-0049-9
https://doi.org/10.1038/s42256-019-0049-9
Autor:
Dominik Niopek, Irina Lehmann, Catharina Gandor, Pauline L. Pfuderer, Max Schwendemann, Max C. Waldhauer, Lukas Adam, Moritz Jakob Przybilla, Jan Mathony, Julius Upmeier zu Belzen, Stefan Holderbach, Mareike D. Hoffmann, Lukas Platz, Carolin Schmelas, Felix Bubeck, Marita Klein, Roland Eils, Thore Bürgel, Daniel Heid, Michael Jendrusch
Publikováno v:
Nature Machine Intelligence. 1:290-290
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d0ec784ba9e24fd0d1e1e15ba3833ee
https://doi.org/10.1038/s42256-019-0063-y
https://doi.org/10.1038/s42256-019-0063-y