Choi JW - Search Results

1

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

Kim HK, Min S, Song M, Jung S, Choi JW, Kim Y, Lee S, Yoon S, Kim HH. Kim HK, et al. Among authors: choi jw. Nat Biotechnol. 2018 Mar;36(3):239-241. doi: 10.1038/nbt.4061. Epub 2018 Jan 29. Nat Biotechnol. 2018. PMID: 29431740

2

SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance.

Kim HK, Kim Y, Lee S, Min S, Bae JY, Choi JW, Park J, Jung D, Yoon S, Kim HH. Kim HK, et al. Among authors: choi jw. Sci Adv. 2019 Nov 6;5(11):eaax9249. doi: 10.1126/sciadv.aax9249. eCollection 2019 Nov. Sci Adv. 2019. PMID: 31723604 Free PMC article.

3

High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells.

Kim HK, Lee S, Kim Y, Park J, Min S, Choi JW, Huang TP, Yoon S, Liu DR, Kim HH. Kim HK, et al. Among authors: choi jw. Nat Biomed Eng. 2020 Jan;4(1):111-124. doi: 10.1038/s41551-019-0505-1. Epub 2020 Jan 14. Nat Biomed Eng. 2020. PMID: 31937939

4

Prediction of the sequence-specific cleavage activity of Cas9 variants.

Kim N, Kim HK, Lee S, Seo JH, Choi JW, Park J, Min S, Yoon S, Cho SR, Kim HH. Kim N, et al. Among authors: choi jw. Nat Biotechnol. 2020 Nov;38(11):1328-1336. doi: 10.1038/s41587-020-0537-9. Epub 2020 Jun 8. Nat Biotechnol. 2020. PMID: 32514125

5

Sequence-specific prediction of the efficiencies of adenine and cytosine base editors.

Song M, Kim HK, Lee S, Kim Y, Seo SY, Park J, Choi JW, Jang H, Shin JH, Min S, Quan Z, Kim JH, Kang HC, Yoon S, Kim HH. Song M, et al. Among authors: choi jw. Nat Biotechnol. 2020 Sep;38(9):1037-1043. doi: 10.1038/s41587-020-0573-5. Epub 2020 Jul 6. Nat Biotechnol. 2020. PMID: 32632303

6

In vivo high-throughput profiling of CRISPR-Cpf1 activity.

Kim HK, Song M, Lee J, Menon AV, Jung S, Kang YM, Choi JW, Woo E, Koh HC, Nam JW, Kim H. Kim HK, et al. Among authors: choi jw. Nat Methods. 2017 Feb;14(2):153-159. doi: 10.1038/nmeth.4104. Epub 2016 Dec 19. Nat Methods. 2017. PMID: 27992409

7

Targeting mutant KRAS with CRISPR-Cas9 controls tumor growth.

Kim W, Lee S, Kim HS, Song M, Cha YH, Kim YH, Shin J, Lee ES, Joo Y, Song JJ, Choi EJ, Choi JW, Lee J, Kang M, Yook JI, Lee MG, Kim YS, Paik S, Kim HH. Kim W, et al. Among authors: choi jw, choi ej. Genome Res. 2018 Jan 11;28(3):374-82. doi: 10.1101/gr.223891.117. Online ahead of print. Genome Res. 2018. PMID: 29326299 Free PMC article.

8

Machine learning model for predicting malaria using clinical information.

Lee YW, Choi JW, Shin EH. Lee YW, et al. Among authors: choi jw. Comput Biol Med. 2021 Feb;129:104151. doi: 10.1016/j.compbiomed.2020.104151. Epub 2020 Nov 28. Comput Biol Med. 2021. PMID: 33290932

9

Correction: Molecular subtypes of oropharyngeal cancer show distinct immune microenvironment related with immune checkpoint blockade response.

Kim MH, Kim JH, Lee JM, Choi JW, Jung D, Cho H, Kang H, Hong MH, Heo SJ, Kim SH, Choi EC, Kim DH, Park YM, Kim S, Yoon SO, Koh YW, Cho BC, Kim HR. Kim MH, et al. Among authors: choi ec, choi jw. Br J Cancer. 2020 Sep;123(6):1041. doi: 10.1038/s41416-020-0944-1. Br J Cancer. 2020. PMID: 32572173 Free PMC article.

10

Investigating the Feasibility of Targeted Next-Generation Sequencing to Guide the Treatment of Head and Neck Squamous Cell Carcinoma.

Lim SM, Cho SH, Hwang IG, Choi JW, Chang H, Ahn MJ, Park KU, Kim JW, Ko YH, Ahn HK, Cho BC, Nam BH, Chun SH, Hong JH, Kwon JH, Choi JG, Kang EJ, Yun T, Lee KW, Kim JH, Kim JS, Lee HW, Kim MK, Jung D, Kim JE, Keam B, Yun HJ, Kim S, Kim HR. Lim SM, et al. Among authors: choi jw, choi jg. Cancer Res Treat. 2019 Jan;51(1):300-312. doi: 10.4143/crt.2018.012. Epub 2018 May 9. Cancer Res Treat. 2019. PMID: 29747488 Free PMC article.

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