Publications
Publications
2026
Park, Y., & Lee, S. (2026). AIM-DDI: A Model-Agnostic Multimodal Integration Module for Drug-Drug Interaction Prediction. arXiv preprint arXiv:2605.14327. [Link]
Sung, I., Bang, D., Kim, S., & Lee, S. (2026). Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors. npj Precision Oncology. [Link]
Yi, J., Choi, S., Kim, S., & Lee, S. (2026). SubDyve: Subgraph-Driven Dynamic Propagation for Virtual Screening Enhancement Controlling False Positive. In The Fourteenth International Conference on Learning Representations (ICLR 2026). [Link]
Lu, Y., Kang, S., Kim, S., & Lee, S. (2026). EnsDTI: Predicting Drug-Target Interaction with Mixture-of-Experts and Confidence Assessment. IEEE Transactions on Computational Biology and Bioinformatics. [Link]
2025
Hong, A., Lee, S., & Kim, K. (2025). Multi-omic relay velocity modeling uncovers dynamic chromatin-transcription regulation across cell states. Nature Communications. [Link]
Koo, B., Sung, I., Lee, S., & Kim, S. (2025). Transcriptome Transformer: improving patient survival prediction via multitask learning of transcriptomic and clinical features. Briefings in Bioinformatics, 26(6), bbaf628. [Link]
Lu, Y., Piao, Y., Lee, S., & Kim, S. (2025). Context-Aware Hierarchical Fusion for Drug Relational Learning. IEEE Transactions on Computational Biology and Bioinformatics. [Link]
Lim, H., Kim, S., & Lee, S. (2025). CheapNet: Cross-attention on Hierarchical representations for Efficient protein-ligand binding Affinity Prediction. In The Thirteenth International Conference on Learning Representations (ICLR 2025). [Link]
Park, S., Lee, S., Pak, M., & Kim, S. (2025). Dual representation learning for predicting drug-side effect frequency using protein target information. IEEE Journal of Biomedical and Health Informatics, 29(3), 1817-1827. [Link]
Sung, I., Lee, S., Bang, D., Yi, J., Lee, S., & Kim, S. (2025). MDTR: a knowledge-guided interpretable representation for quantifying liver toxicity at transcriptomic level. Frontiers in Pharmacology, 15, 1398370. [Link]
2024
Lee, S., Park, J., Piao, Y., Lee, D., Lee, D., & Kim, S. (2024). Multi-layered knowledge graph neural network reveals pathway-level agreement of three breast cancer multi-gene assays. Computational and Structural Biotechnology Journal, 23, 1715-1724. [Link]
Cho, C., Lee, S., Bang, D., Piao, Y., & Kim, S. (2024). ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillation. Scientific Reports, 14(1), 23010. [Link]
Piao, Y., Lee, S., Lu, Y., & Kim, S. (2024). Improving out-of-distribution generalization in graphs via hierarchical semantic environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) (pp. 27631-27640). [Link]
2023
Gu, J., Bang, D., Yi, J., Lee, S., Kim, D. K., & Kim, S. (2023). A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug–drug interaction data and supervised contrastive learning. Briefings in Bioinformatics, 24(5), bbad285. [Link]
Bang, D., Lim, S., Lee, S., & Kim, S. (2023). Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nature Communications, 14(1), 3570. [Link]
Pak, M., Lee, S., Sung, I., Koo, B., & Kim, S. (2023). Improved drug response prediction by drug target data integration via network-based profiling. Briefings in Bioinformatics, 24(2), bbad034. [Link]
Yi, J., Lee, S., Lim, S., Cho, C., Piao, Y., Yeo, M., ... & Lee, S. (2023). Exploring chemical space for lead identification by propagating on chemical similarity network. Computational and structural biotechnology journal, 21, 4187-4195. [Link]
2022
Koo, B., Lee, D., Lee, S., Sung, I., Kim, S., & Lee, S. (2022). Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection. Cancers, 14(17), 4120. [Link]
Moon, J. H., Lee, S., Pak, M., Hur, B., & Kim, S. (2022). Mldeg: A machine learning approach to identify differentially expressed genes using network property and network propagation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(4), 2356-2364. [Link]
Sung, I., Lee, S., Pak, M., Shin, Y., & Kim, S. (2022). 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. BMC bioinformatics, 23(Suppl 3), 149. [Link]
Piao, Y., Lee, S., Lee, D., & Kim, S. (2022, June). Sparse structure learning via graph neural networks for inductive document classification. In Proceedings of the AAAI conference on artificial intelligence (AAAI 2022) (Vol. 36, No. 10, pp. 11165-11173). [Link]
Lee, S., Lee, D., Piao, Y., & Kim, S. SPGP: Structure Prototype Guided Graph Pooling. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning. [Link]
Lim, S., Lee, S., Piao, Y., Choi, M., Bang, D., Gu, J., & Kim, S. (2022). On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Computational and Structural Biotechnology Journal, 20, 4288-4304. [Link]
Kim, J., Lim, S., Lee, S., Cho, C., & Kim, S. (2022, January). Embedding of FDA Approved Drugs in Chemical Space Using Cascade Autoencoder with Metric Learning. In 2022 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 363-365). IEEE. [Link]
2021
Kim, M., Lee, S., Lim, S., Lee, D. Y., & Kim, S. (2021). Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer. Scientific reports, 11(1), 23992. [Link]
Kim, I., Lee, S., Kim, Y., Namkoong, H., & Kim, S. (2021, December). A probabilistic model for pathway-guided gene set selection. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2733-2740). IEEE. [Link]
Jeong, D., Lim, S., Lee, S., Oh, M., Cho, C., Seong, H., ... & Kim, S. (2021). Construction of condition-specific gene regulatory network using kernel canonical correlation analysis. Frontiers in Genetics, 12, 652623. [Link]
Lee, T., Lee, S., Kang, M., & Kim, S. (2021). Deep hierarchical embedding for simultaneous modeling of gpcr proteins in a unified metric space. Scientific Reports, 11(1), 9543. [Link]
Lee, S., Lee, T., Noh, Y. K., & Kim, S. (2021). Ranked k-spectrum kernel for comparative and evolutionary comparison of exons, introns, and cpg islands. IEEE/ACM transactions on computational biology and bioinformatics, 18(3), 1174-1183. [Link]
Park, Y. J., Lee, S., Lim, S., Nahmgoong, H., Ji, Y., Huh, J. Y., ... & Kim, J. B. (2021). DNMT1 maintains metabolic fitness of adipocytes through acting as an epigenetic safeguard of mitochondrial dynamics. Proceedings of the National Academy of Sciences, 118(11), e2021073118. [Link]
~2020
Oh, M., Park, S., Lee, S., Lee, D., Lim, S., Jeong, D., ... & Kim, S. (2020). DRIM: a web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration. Frontiers in Genetics, 11, 564792. [Link]
Kang, M., Lee, S., Lee, D., & Kim, S. (2020). Learning cell-type-specific gene regulation mechanisms by multi-attention based deep learning with regulatory latent space. Frontiers in genetics, 11, 869. [Link]
Lee, S., Lim, S., Lee, T., Sung, I., & Kim, S. (2020). Cancer subtype classification and modeling by pathway attention and propagation. Bioinformatics, 36(12), 3818-3824. [Link]
Hur, B., Kang, D., Lee, S., Moon, J. H., Lee, G., & Kim, S. (2019). Venn-diaNet: venn diagram based network propagation analysis framework for comparing multiple biological experiments. BMC bioinformatics, 20(Suppl 23), 667. [Link]
Kang, D., Ahn, H., Lee, S., Lee, C. J., Hur, J., Jung, W., & Kim, S. (2019). StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis. BMC genomics, 20(Suppl 11), 949. [Link]
Kim, M., Lee, S., Lim, S., & Kim, S. (2019). SpliceHetero: an information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq. PLoS One, 14(10), e0223520. [Link]
Lee, D., Lee, S., & Kim, S. (2019). PRISM: methylation pattern-based, reference-free inference of subclonal makeup. Bioinformatics, 35(14), i520-i529. [Link]
Kang, D., Ahn, H., Lee, S., Lee, C. J., Hur, J., Jung, W., & Kim, S. (2018, December). Identifying stress-related genes and predicting stress types in Arabidopsis using logical correlation layer and CMCL loss through time-series data. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 399-404). IEEE. [Link]
Lim, S., Lee, S., Jung, I., Rhee, S., & Kim, S. (2020). Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data. Briefings in bioinformatics, 21(1), 36-46. [Link]
Lee, C. J., Kang, D., Lee, S., Lee, S., Kang, J., & Kim, S. (2018). In silico experiment system for testing hypothesis on gene functions using three condition specific biological networks. Methods, 145, 10-15. [Link]
Lee, S., Park, Y., & Kim, S. (2017). MIDAS: mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data. Methods, 124, 13-24. [Link]
Moon, J. H., Lim, S., Jo, K., Lee, S., Seo, S., & Kim, S. (2017). PINTnet: construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI. BMC systems biology, 11(Suppl 2), 15. [Link]
Lee, S., Moon, J. H., Park, Y., & Kim, S. (2017, February). Flow maximization analysis of cell cycle pathway activation status in breast cancer subtypes. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 91-95). IEEE. [Link]
Chae, H., Lee, S., Nephew, K. P., & Kim, S. (2016). Subtype-specific CpG island shore methylation and mutation patterns in 30 breast cancer cell lines. BMC systems biology, 10(Suppl 4), 116. [Link]
Chae, H., Lee, S., Seo, S., Jung, D., Chang, H., Nephew, K. P., & Kim, S. (2016). BioVLAB-mCpG-SNP-EXPRESS: a system for multi-level and multi-perspective analysis and exploration of dna methylation, sequence variation (SNPs), and gene expression from multi-omics data. Methods, 111, 64-71. [Link]
Jeong, H. M., Lee, S., Chae, H., Kim, R., Kwon, M. J., Oh, E., ... & Shin, Y. K. (2016). Efficiency of methylated DNA immunoprecipitation bisulphite sequencing for whole-genome DNA methylation analysis. Epigenomics, 8(8), 1061-1077. [Link]
2026
Oh, S., Lee, S. (2026). SPathFusion: Multi-Omics Fusion Model Using Integrated Graphs and Pathway-Enhanced Spatial Modulation. KCC 2026.
Park, Y., Lee, S. (2026). M3-DDI: A Multimodal Module for Durg-Drug Interaction. KCC 2026.
Yang, H., Lee, S. (2026). PPI Graph-Based Encoder for Survival Prediction in Single-Cell RNA Sequencing. KCC 2026.
Wang, S., Lee, S. (2026). Explainable Drug Repurposing via Heterogeneous Graph Learning and Path-Based Context Fusion. KCC 2026.
Ahn, H., Lee, S., Kim, S., Lee, S. (2026). PatchMoe-GC: Residual MoE for Gastic Histopathology Patch Classification. KCC 2026.