Machine learning to promote transparent provenance of genetic engineering
The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying provenance by examining telltale signatures characteristic to different genetic designers, termed genetic engineering attribution, would deter misuse, yet is still considered unsolved....
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/140985 |