A machine learning toolkit for genetic engineering attribution to facilitate biosecurity

Ethan C. Alley (), Miles Turpin, Andrew Bo Liu, Taylor Kulp-McDowall, Jacob Swett, Rey Edison, Stephen E. Stetina, George M. Church and Kevin M. Esvelt
Additional contact information
Ethan C. Alley: Alt. Technology Labs, Inc.
Miles Turpin: Duke University
Andrew Bo Liu: Harvard Medical School
Jacob Swett: Alt. Technology Labs, Inc.
Rey Edison: Massachusetts Institute of Technology
Stephen E. Stetina: Massachusetts Institute of Technology
George M. Church: Alt. Technology Labs, Inc.
Kevin M. Esvelt: Alt. Technology Labs, Inc.

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract 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 recurrent neural networks trained on DNA motifs and basic phenotype data can reach 70% attribution accuracy in distinguishing between over 1,300 labs. To make these models usable in practice, we introduce a framework for weighing predictions against other investigative evidence using calibration, and bring our model to within 1.6% of perfect calibration. Additionally, we demonstrate that simple models can accurately predict both the nation-state-of-origin and ancestor labs, forming the foundation of an integrated attribution toolkit which should promote responsible innovation and international security alike.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-19612-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19612-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-19612-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().