| Deep learning in bioinformatics S Min, B Lee, S Yoon Briefings in bioinformatics 18 (5), 851-869, 2017 | 1926 | 2017 |
| Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity HK Kim, S Min, M Song, S Jung, JW Choi, Y Kim, S Lee, S Yoon, HH Kim Nature biotechnology 36 (3), 239-241, 2018 | 334 | 2018 |
| Predicting the efficiency of prime editing guide RNAs in human cells HK Kim, G Yu, J Park, S Min, S Lee, S Yoon, HH Kim Nature Biotechnology 39 (2), 198-206, 2021 | 225 | 2021 |
| SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance HK Kim, Y Kim, S Lee, S Min, JY Bae, JW Choi, J Park, D Jung, S Yoon, ... Science advances 5 (11), eaax9249, 2019 | 219 | 2019 |
| Pure transformers are powerful graph learners J Kim, D Nguyen, S Min, S Cho, M Lee, H Lee, S Hong Advances in Neural Information Processing Systems 35, 14582-14595, 2022 | 206 | 2022 |
| Prediction of the sequence-specific cleavage activity of Cas9 variants N Kim, HK Kim, S Lee, JH Seo, JW Choi, J Park, S Min, S Yoon, SR Cho, ... Nature Biotechnology 38 (11), 1328-1336, 2020 | 192 | 2020 |
| Deep recurrent neural network-based identification of precursor micrornas S Park, S Min, HS Choi, S Yoon Advances in Neural Information Processing Systems 30, 2017 | 127* | 2017 |
| High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells HK Kim, S Lee, Y Kim, J Park, S Min, JW Choi, TP Huang, S Yoon, DR Liu, ... Nature biomedical engineering 4 (1), 111-124, 2020 | 120 | 2020 |
| Sequence-specific prediction of the efficiencies of adenine and cytosine base editors M Song, HK Kim, S Lee, Y Kim, SY Seo, J Park, JW Choi, H Jang, JH Shin, ... Nature biotechnology 38 (9), 1037-1043, 2020 | 105 | 2020 |
| Learned embeddings from deep learning to visualize and predict protein sets C Dallago, K Schütze, M Heinzinger, T Olenyi, M Littmann, AX Lu, ... Current Protocols 1 (5), e113, 2021 | 91 | 2021 |
| Pre-training of deep bidirectional protein sequence representations with structural information S Min, S Park, S Kim, HS Choi, B Lee, S Yoon IEEE Access 9, 123912-123926, 2021 | 74 | 2021 |
| Generation of a more efficient prime editor 2 by addition of the Rad51 DNA-binding domain M Song, JM Lim, S Min, JS Oh, DY Kim, JS Woo, H Nishimasu, SR Cho, ... Nature communications 12 (1), 5617, 2021 | 70 | 2021 |
| Polyphonic music generation with sequence generative adversarial networks S Lee, U Hwang, S Min, S Yoon arXiv preprint arXiv:1710.11418, 2017 | 46* | 2017 |
| Grounding visual representations with texts for domain generalization S Min, N Park, S Kim, S Park, J Kim European conference on computer vision, 37-53, 2022 | 41 | 2022 |
| Recording of elapsed time and temporal information about biological events using Cas9 J Park, JM Lim, I Jung, SJ Heo, J Park, Y Chang, HK Kim, D Jung, JH Yu, ... Cell 184 (4), 1047-1063. e23, 2021 | 38 | 2021 |
| Deep learning models to predict the editing efficiencies and outcomes of diverse base editors N Kim, S Choi, S Kim, M Song, JH Seo, S Min, J Park, SR Cho, HH Kim Nature Biotechnology 42 (3), 484-497, 2024 | 28 | 2024 |
| Sniper2L is a high-fidelity Cas9 variant with high activity Y Kim, N Kim, I Okafor, S Choi, S Min, J Lee, SM Bae, K Choi, J Choi, ... Nature chemical biology 19 (8), 972-980, 2023 | 27 | 2023 |
| TargetNet: Functional microRNA Target Prediction with Deep Neural Networks S Min, B Lee, S Yoon Bioinformatics, Volume 38, Issue 3, 1 February 2022, Pages 671–677, 2022 | 27 | 2022 |
| Neural universal discrete denoiser T Moon, S Min, B Lee, S Yoon Advances in Neural Information Processing Systems 29, 2016 | 22 | 2016 |
| Protein transfer learning improves identification of heat shock protein families S Min, HG Kim, B Lee, S Yoon Plos one 16 (5), e0251865, 2021 | 20 | 2021 |
Sungroh YoonProfessor, Electrical and Computer Engineering & Artificial Intelligence, Seoul National Universitysnu.ac.kr의 이메일 확인됨