A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2041-1723
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
A machine learning toolkit for genetic engineering attribution to facilitate biosecurity ; volume:11 ; number:1 ; day:8 ; month:12 ; year:2020 ; pages:1-12 ; date:12.2020
Nature Communications ; 11, Heft 1 (8.12.2020), 1-12, 12.2020
- Klassifikation
-
Politik
- Urheber
-
Alley, Ethan C.
Turpin, Miles
Liu, Andrew Bo
Kulp-McDowall, Taylor
Swett, Jacob
Edison, Rey
Stetina, Stephen E. Von
Church, George M.
Esvelt, Kevin
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s41467-020-19612-0
- URN
-
urn:nbn:de:101:1-2021011119433775477450
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:37 MESZ
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Beteiligte
- Alley, Ethan C.
- Turpin, Miles
- Liu, Andrew Bo
- Kulp-McDowall, Taylor
- Swett, Jacob
- Edison, Rey
- Stetina, Stephen E. Von
- Church, George M.
- Esvelt, Kevin
- SpringerLink (Online service)
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