A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
© 2020, The Author(s). 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...
| Main Authors: | , , , , , , , , |
|---|---|
| Formato: | Artigo |
| Idioma: | English |
| Publicado: |
Springer Science and Business Media LLC
2021
|
| Acceso en liña: | https://hdl.handle.net/1721.1/134004 |