Seonwoo Min
Seonwoo Min
Unknown affiliation
No verified email
Cited by
Cited by
Deep learning in bioinformatics
S Min, B Lee, S Yoon
Briefings in bioinformatics 18 (5), 851-869, 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, H Kim
Nature biotechnology 36 (3), 239-241, 2018
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
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
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
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
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
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
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
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
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
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
Polyphonic music generation with sequence generative adversarial networks
S Lee, U Hwang, S Min, S Yoon
arXiv preprint arXiv:1710.11418, 2017
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
Neural universal discrete denoiser
T Moon, S Min, B Lee, S Yoon
Advances in Neural Information Processing Systems 29, 2016
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
Bag of tricks for electrocardiogram classification with deep neural networks
S Min, HS Choi, H Han, M Seo, JK Kim, J Park, S Jung, IY Oh, B Lee, ...
2020 Computing in Cardiology, 1-4, 2020
Learning-based instantaneous drowsiness detection using wired and wireless electroencephalography
HS Choi, S Min, S Kim, H Bae, JE Yoon, I Hwang, D Oh, CH Yun, S Yoon
IEEE Access 7, 146390-146402, 2019
Protein transfer learning improves identification of heat shock protein families
S Min, HG Kim, B Lee, S Yoon
Plos one 16 (5), e0251865, 2021
Towards high generalization performance on electrocardiogram classification
H Han, S Park, S Min, HS Choi, E Kim, H Kim, S Park, J Kim, J Park, J An, ...
2021 Computing in Cardiology (CinC) 48, 1-4, 2021
The system can't perform the operation now. Try again later.
Articles 1–20