A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.

Alley EC, Turpin M, Liu AB, Kulp-McDowall T, Swett J, Edison R, Von Stetina SE, Church GM, Esvelt KM

DOI: 10.1038/s41467-020-19612-0
Published: 2020 Dec 8
Container: Nature communications
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41467-020-19612-0,
  title = {A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.},
  author = {Alley EC and Turpin M and Liu AB and Kulp-McDowall T and Swett J and Edison R and Von Stetina SE and Church GM and Esvelt KM},
  year = {2020},
  journal = {Nature communications},
  doi = {10.1038/s41467-020-19612-0},
  url = {https://doi.org/10.1038/s41467-020-19612-0}
}

RIS

TY  - JOUR
TI  - A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.
AU  - Alley EC
AU  - Turpin M
AU  - Liu AB
AU  - Kulp-McDowall T
AU  - Swett J
AU  - Edison R
AU  - Von Stetina SE
AU  - Church GM
AU  - Esvelt KM
PY  - 2020
JO  - Nature communications
DO  - 10.1038/s41467-020-19612-0
UR  - https://doi.org/10.1038/s41467-020-19612-0
ER  -

APA

EC, A., M, T., AB, L., T, K., J, S., R, E., SE, V. S., GM, C., & KM, E. (2020). A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.. Nature communications. https://doi.org/10.1038/s41467-020-19612-0

Source records

pubmed · retrieved 2026-09-26T07:44:16.305Z