A machine learning toolkit for genetic engineering attribution to facilitate biosecurity

The potential for accidental or deliberate misuse of biotechnology is of concern for international biosecurity. Here the authors apply machine learning to DNA sequences and associated phenotypic data to facilitate genetic engineering attribution and identify country-of-origin and ancestral lab of en...

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Autores principales:	Ethan C. Alley, Miles Turpin, Andrew Bo Liu, Taylor Kulp-McDowall, Jacob Swett, Rey Edison, Stephen E. Von Stetina, George M. Church, Kevin M. Esvelt
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/0e70fbf60f804bed8ad5841f3c3b9f29
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id	oai:doaj.org-article:0e70fbf60f804bed8ad5841f3c3b9f29
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spelling	oai:doaj.org-article:0e70fbf60f804bed8ad5841f3c3b9f292021-12-02T14:16:05ZA machine learning toolkit for genetic engineering attribution to facilitate biosecurity10.1038/s41467-020-19612-02041-1723https://doaj.org/article/0e70fbf60f804bed8ad5841f3c3b9f292020-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19612-0https://doaj.org/toc/2041-1723The potential for accidental or deliberate misuse of biotechnology is of concern for international biosecurity. Here the authors apply machine learning to DNA sequences and associated phenotypic data to facilitate genetic engineering attribution and identify country-of-origin and ancestral lab of engineered DNA sequences.Ethan C. AlleyMiles TurpinAndrew Bo LiuTaylor Kulp-McDowallJacob SwettRey EdisonStephen E. Von StetinaGeorge M. ChurchKevin M. EsveltNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ethan C. Alley Miles Turpin Andrew Bo Liu Taylor Kulp-McDowall Jacob Swett Rey Edison Stephen E. Von Stetina George M. Church Kevin M. Esvelt A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
description	The potential for accidental or deliberate misuse of biotechnology is of concern for international biosecurity. Here the authors apply machine learning to DNA sequences and associated phenotypic data to facilitate genetic engineering attribution and identify country-of-origin and ancestral lab of engineered DNA sequences.
format	article
author	Ethan C. Alley Miles Turpin Andrew Bo Liu Taylor Kulp-McDowall Jacob Swett Rey Edison Stephen E. Von Stetina George M. Church Kevin M. Esvelt
author_facet	Ethan C. Alley Miles Turpin Andrew Bo Liu Taylor Kulp-McDowall Jacob Swett Rey Edison Stephen E. Von Stetina George M. Church Kevin M. Esvelt
author_sort	Ethan C. Alley
title	A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
title_short	A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
title_full	A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
title_fullStr	A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
title_full_unstemmed	A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
title_sort	machine learning toolkit for genetic engineering attribution to facilitate biosecurity
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/0e70fbf60f804bed8ad5841f3c3b9f29
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A machine learning toolkit for genetic engineering attribution to facilitate biosecurity

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