| Deep learning in bioinformatics S Min, B Lee, S Yoon Briefings in bioinformatics 18 (5), 851-869, 2017 | 1926 | 2017 |
| Towards a rigorous evaluation of time-series anomaly detection S Kim, K Choi, HS Choi, B Lee, S Yoon Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7194-7201, 2022 | 178 | 2022 |
| deepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks B Lee, J Baek, S Park, S Yoon Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB …, 2016 | 127 | 2016 |
| Biometric authentication using noisy electrocardiograms acquired by mobile sensors HS Choi, B Lee, S Yoon IEEE access 4, 1266-1273, 2016 | 121 | 2016 |
| LncRNAnet: long non-coding RNA identification using deep learning J Baek, B Lee, S Kwon, S Yoon Bioinformatics 34 (22), 3889-3897, 2018 | 114 | 2018 |
| DUDE-Seq: Fast, flexible, and robust denoising for targeted amplicon sequencing B Lee, T Moon, S Yoon, T Weissman PLOS ONE 12 (7), 2017 | 77 | 2017 |
| CASPER: context-aware scheme for paired-end reads from high-throughput amplicon sequencing S Kwon, B Lee, S Yoon BMC bioinformatics 15 (Suppl 9), S10, 2014 | 76 | 2014 |
| 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 |
| DNA-level splice junction prediction using deep recurrent neural networks B Lee, T Lee, B Na, S Yoon arXiv preprint arXiv:1512.05135, 2015 | 56 | 2015 |
| In-depth analysis of interrelation between quality scores and real errors in illumina reads S Kwon, S Park, B Lee, S Yoon 2013 35th annual international conference of the IEEE engineering in …, 2013 | 49 | 2013 |
| TargetNet: functional microRNA target prediction with deep neural networks S Min, B Lee, S Yoon Bioinformatics 38 (3), 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 |
| 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 | 20 | 2020 |
| hc-OTU: A fast and accurate method for clustering operational taxonomic units based on homopolymer compaction S Park, H Choi, B Lee, J Chun, JH Won, S Yoon IEEE/ACM transactions on computational biology and bioinformatics 15 (2 …, 2016 | 17 | 2016 |
| Dna steganalysis using deep recurrent neural networks H Bae, B Lee, S Kwon, S Yoon BIOCOMPUTING 2019: Proceedings of the Pacific Symposium, 88-99, 2018 | 15 | 2018 |
| Flexible dual-branched message-passing neural network for a molecular property prediction J Jo, B Kwak, B Lee, S Yoon ACS omega 7 (5), 4234-4244, 2022 | 11 | 2022 |
| GeoT: a geometry-aware transformer for reliable molecular property prediction and chemically interpretable representation learning B Kwak, J Park, T Kang, J Jo, B Lee, S Yoon ACS omega 8 (42), 39759-39769, 2023 | 10* | 2023 |
| Contrastive Time-Series Anomaly Detection HG Kim, S Kim, S Min, B Lee IEEE Transactions on Knowledge and Data Engineering, 2023 | 9 | 2023 |
| Tictok: Time-series anomaly detection with contrastive tokenization M Kang, B Lee IEEE Access 11, 81011-81020, 2023 | 5 | 2023 |
