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Dr. Kim is a professor in the Department of Pharmacology, Yonsei University College of Medicine, Seoul, South Korea. He received his M.D. in 2001 and Ph.D. in 2006 from Yonsei University, Seoul. During his Ph.D. program, he studied tissue engineering using mesenchymal stem cells and biomaterials. After postdoctoral training at Emory University, Atlanta, Georgia, USA, in the field of stem cell biology, he became an independent researcher in 2010, when he changed his research field to genome editing. His laboratory is interested in genome engineering in several types of cultured cells (including stem cells) and in mammals, as well as in the development of CRISPR-Cas-based high-throughput methods for genetic studies. Dr. Kim moved to Yonsei University in 2015.
His group developed a high-throughput method for profiling Cpf1 activity in human cells. Furthermore, his group also developed deep learning-based computational models predicting Cpf1 activity at endogenous sites with unprecedentedly high accuracy. Recently, his group generated computational models that predict the efficiencies of various Cas9 variants in a highly comparative manner (Kim et al. Nat. Biotechnol. 2020) and expanded the high-throughput evaluation and computational modeling to base editors (Song et al. Nat. Biotechnol. 2020). Nowadays, high-throughput evaluation of RNA-guided genome editing tool activities and deep learning-based computational model development, both of which were pioneered by Dr. Kim's group, are widely used by other genome editing scientists over the world. Ongoing projects in his lab include the development of potential therapeutic applications of CRISPR-Cas9 to various genetic diseases in collaboration with clinicians. His group will continue the research to improve or develop genome editing tools for biomedical research and biotechnology and apply these advanced genome editing methods as therapeutic modalities for various diseases.

Representative papers
- Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. (2020) Nat. Biotechnol. doi: 10.1038/ s41587-020-0573-5
- Prediction of the sequence-specific cleavage activity of Cas9 variants. (2020). Nat. Biotechnol. doi.org/10.1038/s41587-020- 0537-9.
- High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells. (2020). Nat. Biomed. Eng. 4(1):111-124.
- Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. (2018). Nat. Biotechnol. 36: 239-241.

   

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