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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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722865/
https://www.ncbi.nlm.nih.gov/pubmed/33293535
http://dx.doi.org/10.1038/s41467-020-19612-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722865/
https://www.ncbi.nlm.nih.gov/pubmed/33293535
http://dx.doi.org/10.1038/s41467-020-19612-0

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