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성균관대학교

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최종업데이트 2023.05.09.

Prediction of efficiencies for diverse prime editing systems in multiple cell types
- Cell, 2023 Apr 21;S0092-8674(23)00331-8 | https://doi.org/10.1016/j.cell.2023.03.034
- 2023-04
- 의약학
- 한빛사논문
Predicting the efficiency of prime editing guide RNAs in human cells
- Nat. Biotechnol., Published: 21 September 2020 | https://doi.org/10.1038/s41587-020-0677-y
- 2020-09
- 의약학
- 한빛사논문
Sequence-specific prediction of the efficiencies of adenine and cytosine base editors
- Nat. Biotechnol., Published: 06 July 2020 | https://doi.org/10.1038/s41587-020-0573-5
- 2020-07
- 의약학
- 한빛사논문
Prediction of the sequence-specific cleavage activity of Cas9 variants
- Nat. Biotechnol., Published: 08 June 2020 | https://doi.org/10.1038/s41587-020-0537-9
- 2020-06
- 의약학
- 한빛사논문
High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells
- Nat. Biomed. Eng., Published : 14 January 2020 | DOI https://doi.org/10.1038/s41551-019-0505-1
- 2020-01
- 의약학
- 한빛사논문
SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance
- Sci. Adv., 06 Nov 2019 : Vol. 5, no. 11, eaax9249 | DOI: 10.1126/sciadv.aax9249
- 2019-11
- 의약학
- 한빛사논문
Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity
- Nat. Biotechnol., volume 36, pages 239–241 (2018). Published online:29 January 2018, doi:10.1038/nbt.4061
- 2018-01
- 의약학
- 한빛사논문, 상위피인용논문
In vivo high-throughput profiling of CRISPR–Cpf1 activity
- Nat. Methods, 14, 153-159 (2017) | doi:10.1038/nmeth.4104
- 2016-12
- 의약학
- 한빛사논문, 상위피인용논문

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