| Deep Learning in Bioinformatics S Min, B Lee, S Yoon Briefings in Bioinformatics 18 (5), 851-869, 2016 | 1363 | 2016 |
| FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference J Lee, E Kim, S Lee, J Lee, S Yoon IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5267-5276, 2019 | 340 | 2019 |
| RNA design rules from a massive open laboratory J Lee, W Kladwang, M Lee, D Cantu, M Azizyan, H Kim, A Limpaecher, ... Proceedings of the National Academy of Sciences 111 (6), 2122-2127, 2014 | 298 | 2014 |
| How generative adversarial networks and their variants work: An overview Y Hong, U Hwang, J Yoo, S Yoon ACM Computing Surveys (CSUR) 52 (1), 1-43, 2019 | 269 | 2019 |
| Got target?: computational methods for microRNA target prediction and their extension H Min, S Yoon Experimental & molecular medicine 42 (4), 233-244, 2010 | 222 | 2010 |
| 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 | 221 | 2018 |
| Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search J Kim, S Kim, J Kong, S Yoon NeurIPS 2020 Oral (arXiv preprint arXiv:2005.11129), 2020 | 196 | 2020 |
| Prediction of regulatory modules comprising microRNAs and target genes S Yoon, G De Micheli Bioinformatics 21 (suppl_2), ii93-ii100, 2005 | 187 | 2005 |
| Spiking-YOLO: Spiking Neural Network for Real-time Object Detection S Kim, S Park, B Na, S Yoon AAAI 2020 (arXiv preprint arXiv:1903.06530), 2020 | 179* | 2020 |
| Patch SVDD: Patch-level SVDD for anomaly detection and segmentation J Yi, S Yoon Proceedings of the Asian Conference on Computer Vision, 2020 | 171 | 2020 |
| FloWaveNet : A Generative Flow for Raw Audio S Kim, S Lee, J Song, S Yoon ICML 2019 (arXiv preprint arXiv:1811.02155), 2018 | 155 | 2018 |
| LSTM-based system-call language modeling and robust ensemble method for designing host-based intrusion detection systems G Kim, H Yi, J Lee, Y Paek, S Yoon arXiv preprint arXiv:1611.01726, 2016 | 119 | 2016 |
| Big/little deep neural network for ultra low power inference E Park, D Kim, S Kim, YD Kim, G Kim, S Yoon, S Yoo 2015 International Conference on Hardware/Software Codesign and System …, 2015 | 116 | 2015 |
| 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 | 115 | 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 | 106 | 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 | 105 | 2020 |
| ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models J Choi, S Kim, Y Jeong, Y Gwon, S Yoon ICCV 2021 Oral (arXiv preprint arXiv:2108.02938), 2021 | 102 | 2021 |
| Biometric authentication using noisy electrocardiograms acquired by mobile sensors HS Choi, B Lee, S Yoon IEEE access 4, 1266-1273, 2016 | 98 | 2016 |
| HiTRACE: high-throughput robust analysis for capillary electrophoresis S Yoon, J Kim, J Hum, H Kim, S Park, W Kladwang, R Das Bioinformatics 27 (13), 1798-1805, 2011 | 97 | 2011 |
| Deep recurrent neural network-based identification of precursor microRNAs S Park, S Min, HS Choi, S Yoon Advances in Neural Information Processing Systems (NIPS), 2891-2900, 2017 | 96* | 2017 |
Byunghan LeeSeoul National University of Science and TechnologyVerified email at seoultech.ac.kr
