| A review on compound-protein interaction prediction methods: data, format, representation and model S Lim, Y Lu, CY Cho, I Sung, J Kim, Y Kim, S Park, S Kim Computational and Structural Biotechnology Journal 19, 1541-1556, 2021 | 100 | 2021 |
| Cancer subtype classification and modeling by pathway attention and propagation S Lee, S Lim, T Lee, I Sung, S Kim Bioinformatics 36 (12), 3818-3824, 2020 | 48 | 2020 |
| Identification of conserved gene-regulatory networks that integrate environmental sensing and growth in the root cambium NV Hoang, G Choe, Y Zheng, ACA Fandino, I Sung, J Hur, M Kamran, ... Current Biology 30 (15), 2887-2900. e7, 2020 | 34 | 2020 |
| Improved drug response prediction by drug target data integration via network-based profiling M Pak, S Lee, I Sung, B Koo, S Kim Briefings in Bioinformatics 24 (2), bbad034, 2023 | 12 | 2023 |
| Inferring transcriptomic cell states and transitions only from time series transcriptome data K Jo, I Sung, D Lee, H Jang, S Kim Scientific Reports 11 (1), 12566, 2021 | 9 | 2021 |
| AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning I Sung, S Lee, M Pak, Y Shin, S Kim BMC bioinformatics 23 (Suppl 3), 149, 2022 | 5 | 2022 |
| DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network M Pak, D Bang, I Sung, S Kim, S Lee Frontiers in Genetics 15, 1441558, 2024 | | 2024 |
| Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection B Koo, D Lee, S Lee, I Sung, S Kim, S Lee Cancers 14 (17), 4120, 2022 | | 2022 |
| MDTR: A Knowledge-Guided Interpretable Representation for Quantifying Liver Toxicity at Transcriptomic Level I Sung, S Lee, D Bang, J Yi, S Lee, S Kim Frontiers in Pharmacology 15, 1398370, 0 | | |
| AutoCoV: Tracking the Early spread of COVID-19 in Terms of the Spatial and Temporal Dynamics from Embedding Space by K-mer Based Deep Learning I Sung, S Lee, M Pak, Y Shin, S Kim | | |
