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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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| Publié dans: | Nat Commun |
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| Auteurs principaux: | , , , , , , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Nature Publishing Group UK
2020
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| Sujets: | |
| Accès en ligne: | 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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