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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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| Gepubliceerd in: | Nat Commun |
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| Hoofdauteurs: | , , , , , , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
Nature Publishing Group UK
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7722865/ https://ncbi.nlm.nih.gov/pubmed/33293535 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-19612-0 |
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