Attribution of genetic engineering: a practical and accurate machine learning toolkit for biosecurity
Alley, E. C.; Turpin, M.; Liu, A. B.; Kulp-McDowall, T.; Swett, J.; Edison, R.; Von Stetina, S. E.; Church, G. M.; Esvelt, K. M.
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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 can reach 70% attribution accuracy 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.
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