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Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

Biology

Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

T. Yuan, N. Yan, et al.

Discover groundbreaking advancements in base editing technology! This study reveals efficient and precise C-to-G base editors engineered for high fidelity and predictable outcomes, making a significant leap in genetic editing. Conducted by an expert team including authors from Shenzhen and Shanghai institutes, these findings pave the way for enhanced genetic modifications in various applications.... show more
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Citations
54
Influential Citations
3
Reference Count
39
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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