Optimal protospacer sequences recommended by ensemble deep learning for high-efficiency base editing
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- Optimal protospacer sequences recommended by ensemble deep learning for high-efficiency base editing
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New York, NY, United States
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- start-up funding grant at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
- National Innovation Fellow Program of the MOST of China
- MOE AcRF Tier 2 award
- MOE AcRF Tier 1 award
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