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A machine learning toolkit for genetic engineering attribution to facilitate biosecurity

The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed ‘genetic engineering attribution’, would deter misuse, yet is still considered unsolved. Here, we show that re...

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Détails bibliographiques
Publié dans:Nat Commun
Auteurs principaux: Alley, Ethan C., Turpin, Miles, Liu, Andrew Bo, Kulp-McDowall, Taylor, Swett, Jacob, Edison, Rey, Von Stetina, Stephen E., Church, George M., Esvelt, Kevin M.
Format: Artigo
Langue:Inglês
Publié: Nature Publishing Group UK 2020
Sujets:
Accès en ligne:https://ncbi.nlm.nih.gov/pmc/articles/PMC7722865/
https://ncbi.nlm.nih.gov/pubmed/33293535
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-19612-0
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