Publications

In Review (*: Equal contributors)

• [1] Lee S, Piao Y, Lee D, Kim S. Multi-Task Informed Learnable Prototypes on Few Shot learning for Molecular Property Prediction
• [2] Cho C*, Lee S*, Piao Y, Bang D, Kim S. ChemAP: Chemical Structure-Based Deep Learning Model via Knowledge Distillation for Predicting Drug Approval before Clinical Trial Phase
• [3] Sung I*, Lee S*, Bang D, Yi J, Kim S. MDTR: A Knowledge-Guided Interpretable Representation for Quantifying Liver Toxicity at Transcriptomic Level
• [4] Shin Y*, Lee S*, Pak M, Cho C, Kim S. DrugPT-Net: Drug Perturbation Guided Visible Neural Network for Drug Response Prediction at Transcriptomic Level
• [5] Lu Y*, Lee S*, Park S, Yi J, Cho C, Lim S, Kang S, Kim S. An ensemble strategy to improve generalization power of deep learning models for DTI prediction
• [6] Lu Y, Piao Y, Lee S, Kim S. Context-Aware Hierarchical Fusion for Drug Relational Learning

International Journals/Conferences (*: Equal contributors) [IF: publication time] [Citation: Google Scholar]

• [1] Lee S*, Park J*, Piao Y, Lee D, Lee D, Kim S. Multi-layered Knowledge Graph Neural Network Reveals Pathway-level Agreement of Three Breast Cancer Multi-gene Assays. Computational and Structural Biotechnology Journal. Apr. 22, 2024; 23:1715-1724 [SCIE, IF=6.0, JCR 20.9%]
• [2] Piao Y, Lee S, Lu Y, Kim S. Improving Out-of-Distribution Generalization in Graphs vis Hierarchical Semantic Environments. CVPR 2024. Acceted. Jun. 17, 2024.
• [3] Park S*, Lee S*, Pak M, Kim S. Dual Representation Learning for Predicting Drug-side Effect Frequency using Protein Target Information. IEEE Journal of Biomedical and Health Informatics. Jan. 5, 2024. [SCIE, IF=7.7, JCR 6.4%]
• [4] Yi J, Lee S, Lim S, Cho C, Piao Y, Yeo M, Kim D, Kim S, Lee S. Exploring chemical space for lead identification by propagating on chemical similarity network. Computational and Structural Biotechnology Journal. Aug. 25, 2023; 21:4187-4195. [SCIE, IF=6.0, JCR 20.9%]
• [5] Gu J*, Bang D*, Yi J*, Lee S, Kim DK, Kim S. A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug–drug interaction data and supervised contrastive learning. Briefings in Bioinformatics. Aug. 7, 2023; 24(5):bbad285. [SCIE, IF=9.5, JCR 3.2%]
• [6] Bang D, Lim S, Lee S, Kim S. Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nature Communications. Jun. 15, 2023; 14(1):3570. [SCIE, IF=17.694, JCR 7.43%] [Citation: 9]
• [7] Pak M*, Lee S*, Sung I, Koo B, Kim S. Improved drug response prediction by drug target data integration via network-based profiling. Briefings in Bioinformatics. Feb. 8, 2023; 24(2):bbad034. [SCIE, IF=13.994, JCR 0.88%] [Citation: 3]
• [8] Lee S, Lee D, Piao Y, Kim S. SPGP: Structure Prototype Guided Graph Pooling. NeurIPS 2022 Workshop New Frontiers in Graph Learning (GLFrontiers). Dec. 2, 2022. [Citation: 1]
• [9] Koo B, Lee D, Lee S, Sung I, Kim S, Lee S. Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection. Cancers. [From The 10th International Conference on Intelligent Biology and Medicine (ICIBM 2022)]. Aug. 25, 2022; 14(17):4120. [SCIE, IF=6.575, JCR 24.29%]
• [10] Lim S*, Lee S*, Piao Y, Choi MG, Bang D, Gu J, Kim S. On Modeling and Utilizing Chemical Compound Information with Deep Learning Technologies: A Task-oriented Approach. Computational and Structural Biotechnology Journal. Aug. 4, 2022; 20:4288-4304. [SCIE, IF=6.155, JCR 23.4%] [Citation: 7]
• [11] Moon JH, Lee S, Pak M, Hur B, Kim S. MLDEG: A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network Propagation. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Jul.-Aug., 2022; 19(4). [SCIE, IF=3.702, JCR 11.60%] [Citation: 5]
• [12] Piao Y, Lee S, Lee D, Kim S. Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification. AAAI 2022. Feb. 22, 2022. [Citation: 20]
• [13] Sung I*, Lee S*, Pak M, Shin Y, Kim S. AutoCoV: Learning the Spatial and Temporal Dynamics of COVID-19 Spread Patterns from Embedding Space by k-mer Based Deep Learning. BMC Bioinformatics. [From The 10th International Conference on Intelligent Biology and Medicine (ICIBM 2022)]. Apr., 2022; 23(3):149. [SCIE, IF=3.169, JCR 26.72%] [Citation: 2]
• [14] Kim J, Lim S, Lee S, Cho C, Kim S. Embedding of FDA Approved Drugs in Chemical Space Using Cascade Autoencoder with Metric Learning. IEEE BigComp 2022. AI-BioHealth 2022 workshop. Jan. 17, 2022
• [15] Kim I, Lee S, Kim Y, Namkoong H, Kim S. Probabilistic model for pathway-guided gene set selection. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 12th International Workshop on Biomedical and Health Informatics (BHI 2021). Dec. 9, 2021. [Citation: 1]
• [16] Kim MS, Lee S, Lim S, Lee DY, Kim S. Subnetwork Representation Learning for Discovering Network Biomarkers in Predicting Lymph Node Metastasis in Early Oral Cancer. Scientific Reports. Dec., 2021; 11:23992. [SCIE, IF=4.380, JCR 22.92%] [Citation: 2]
• [17] Lee T, Lee S, Kang M, Sun Kim. Deep Hierarchical Embedding for Simultaneous Modeling of GPCR Proteins in a Unified Metric Space. Scientific Reports. May., 2021; 11:9543. [SCIE, IF=3.998, JCR 23.24%] [Citation: 3]
• [18] Lee S, Lee T, Noh YK, Kim S. Ranked k-spectrum kernel for comparative and evolutionary comparison of exons, introns, and CpG islands. IEEE/ACM Transactions on Computational Biology and Bioinformatics. May.-Jun., 2021; 18(30). [SCIE, IF=3.015, JCR 8.47%] [Citation: 4]
• [19] Jeong D, Lim S, Lee S, Oh M, Cho C, Seong H, Jung W, Kim S. Construction of Condition-Specific Gene Regulatory Network using Kernel Canonical Correlation Analysis. Frontiers in Genetics. May., 2021; 12:652623. [SCIE, IF=3.260, JCR 41.85%] [Citation: 2]
• [20] Park YJ, Lee S*, Lim S*, Hahn N, Ji Y, Huh JY, Alfadda AA, Kim S, Kim JB. DNMT1 maintains metabolic fitness of adipocytes through acting as an epigenetic safeguard of mitochondrial dynamics. Proceedings of the National Academy of Sciences (PNAS). Mar. 16, 2021; 118(11):e 2021073118. [SCIE, IF=9.412, JCR 10.56%] [Citation: 28]
• [21] Oh M, Park S, Lee S, Lee D, Lim S, Jeong D, Jo K, Jung I, Kim S. DRIM: A web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration. Frontiers in Genetics. Nov., 2020; 11:564792. [SCIE, IF=3.260, JCR 41.85%] [Citation: 12]
• [22] Kang M*, Lee S*, Lee D, Kim S. Learning Cell-Type-Specific Gene Regulation Mechanisms by Multi-Attention Based Deep Learning with Regulatory Latent Space. Frontiers in Genetics. Sep. 30, 2020; 11:869. [SCIE, IF=3.260, JCR 4.185%] [Citation: 15]
• [23] Lee S, Lim S, Lee T, Sung I, Kim S. Cancer subtype classification and modeling by pathway attention and propagation. Bioinformatics. Jun. 15, 2020; 36(12):3818-3824. [SCIE, IF=4.531, JCR 5.93%] [Citation: 42]
• [24] Lim S, Lee S, Jung I, Rhee S, Kim S. Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data. Briefings in Bioinformatics. Jan., 2020; 21(1):36-46. [SCIE, IF=9.101, JCR 0.85%] [Citation: 34]
• [25] Hur B, Kang D, Lee S, Moon JH, Lee G, Kim S. Venn-diaNet: Venn diagram based network propagation analysis framework for computing multiple biological experiments. BMC Bioinformatics [From Proceeding of 30th International Conference on Genome Informatics (GIW)]. Dec. 27, 2019; 20(23):1-12. [SCIE, IF=2.511, JCR 14.41%] [Citation: 23]
• [26] Kang D, Ahn H, Lee S, Lee CJ, Hur J, Jung W, Kim S. StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in Arabidopsis. BMC Genomics [From Proceeding of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)]. Dec. 20, 2019; 20(11):949. [SCIE, IF=3.501, JCR 29.32%] [Citation: 3]
• [27] Kim MS, Lee S, Lim S, Kim S. SpliceHetero: An information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq. Plos ONE. Oct. 23, 2019; 14(10). [SCIE, IF=2.776, JCR 34.06%] [Citation: 11]
• [28] Lee DH. Lee S, Kim S. PRISM: Methylation Pattern-based, Reference-free Inference of Subclonal Makeup. Bioinformatics [From Proceeding of 27th conference on Intelligent Systems for Molecular Biology (ISMB)]. Jul. 5, 2019; 35(14):i520-i529. [SCI, IF=4.531, JCR 5.93%] [Citation: 10]
• [29] Lee CJ, Kang D, Lee S, Lee S, Kang J, Kim S. In silico experiment system for testing hypothesis on gene functions using three condition specific biological networks. Methods. May. 25, 2018; 145:10-15. [SCI, IF=3.998, JCR 14.56%] [Citation: 2]
• [30] Lee S, Park Y, Kim S. MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data. Methods. Jul. 15, 2017; 124:13-24. [SCI, IF=3.802, JCR 22.73%] [Citation: 19]
• [31] Moon JH, Lim S, Jo K, Lee S, Seo S, Kim S. PINTnet: construction of condition-specic pathway interaction network by computing shortest paths on weighted PPI. BMC Systems Biology [From Proceeding of 15th Asia Pacific Bioinformatics Conference (APBC)]. Mar. 14, 2017; 11(2):15. [SCI, IF=2.208, JCR 18.75%] [Citation: 16]
• [32] Lee S, Moon JH, Park Y, Kim S. Flow maximization analysis of cell cycle pathway activation status in breast cancer subtypes. IEEE International Conference on Big Data and Smart Computing (BigComp) 2017. Feb. 13-16, 2017. Jeju, Korea. [Citation: 1]
• [33] Chae H, Lee S, Nephew KP, Kim S, Subtype-specific CpG island shore methylation and mutation patterns in 30 breast cancer cell lines. BMC Systems Biology [From Proceeding of 27th International Conference on Genome Informatics (GIW)]. Dec. 23, 2016; 10(4):116. [SCI, IF=2.208, JCR 18.75%] [Citation: 17]
• [34] Chae H, Lee S, Seo S, Jung D, Chang H, Nephew KP, Kim S. 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. Jul. 28, 2016; 111:64-71. [SCI, IF=3.503, JCR 22.73%] [Citation: 5]
• [35] Jeong HM, Lee S, Chae H, Kim R, Kwon MJ, Oh E, Choi YL, Kim S, Shin YK. Efficiency of methylated DNA immunoprecipitation bisulphite sequencing for whole-genome DNA methylation analysis. Epigenomics. Jun. 8, 2016; 8(8):1061-1077. [SCIE, IF=4.044, JCR 10.78%] [Citation: 18]

Book Chapters [Citation: Google Scholar]

• [1] Pak M, Jeong D, Moon JH, Ann H, Hur B, Lee S, Kim S. Network Propagation for the Analysis of Multi-Omics Data. Recent Advances in Biological Network Analysis (pp. 185-217). 2021 [Citation: 8]

Domestic Journals/Conferences (*: equal contributors)

• [1] 이상선, 박은화, 이도훈, 김선. Multi-Task Aware Learnable Prototypes on Few Shot Learning for Molecular Property Prediction. BIOINFO 2023. 한국생명정보학회. Nov. 13-15, 2023.
• [2] 조창연*, 이상선*, 박은화, 방동민, 김선. A Novel Framework for Drug Approval Prediction using Chemical Structure with Knowledge Distillation. BIOINFO 2023. 한국생명정보학회. Nov. 13-15, 2023.
• [3] 박은화, 이상선, 육예진수, 김선. Improving Out-of-Distribution Generalization in Molecule Graphs with Hierarchical Semantic Environments. BIOINFO 2023. 한국생명정보학회. Nov. 13-15, 2023.
• [4] 신여진*, 이상선*, 박민우, 김선. Enhancing drug response prediction and interpretability through gene ontology and drug target information integration. BIOINFO 2023. 한국생명정보학회. Nov. 13-15, 2023.
• [5] 김도현, 방동민, 이상선, 김선. FragGNN: Enhancing Molecular Property Prediction through Fragmentbased Hierarchical Graph Neural Networks. BIOINFO 2023. 한국생명정보학회. Nov. 13-15, 2023.
• [6] 성인영*, 이상선*, 방동민, 김선. Identification of toxic signature in transcriptomic space. BIOINFO 2022. 한국생명정보학회. Oct. 19-21, 2022.
• [7] 성인영*, 이상선*, 방동민, 김선. Identification of toxic signature in transcriptomic space. AI KOREA 2022. 한국정보과학회 인공지능소사이어티. Aug. 17, 2022.
• [8] 조창연*, 이상선*, 김선. 그래프 기반 Positive-Unlabeled 학습 모델의 앙상블 체계를 사용한 철회 약물 예측. 한국정보과학회 2022 한국컴퓨터종합학술대회 논문집, Vol.49, pp.777-779. Jun., 2022.
• [9] 박민우, 이상선, 성인영, 신윤열, 정인욱, 김선. 밀도기반 군집화와 딥러닝 모델을 이용한 COVID-19 바이러스 전장 유전체 임베딩 전략. Journal of KIISE [From 2021 한국컴퓨터종합학술대회] 제49권 제4호, pp.261-270. 2022.
• [10] 박은화*, 이상선*, 김선. 신약개발과 맞춤의학을 위한 그래프 학습 기술. 한국정보과학회지 제40권 제3호, pp.30-40. 2022.
• [11] 성인영, 이상선, 이도훈, 김선. 네트워크 전파 및 랜덤 포레스트를 활용한 유방암 환자 생존 분석. 한국정보과학회 2021 한국컴퓨터종합학술대회 논문집, Vol.48, pp.609-611. Jun., 2021.
• [12] 박은화, 이상선, 김선. 헬스케어 예측을 위한 전자 건강 기록 기반 그래프 컨볼루션 모델. 한국정보과학회 2020 한국컴퓨터종합학술대회 논문집, Vol.47, pp.569-571. Jul., 2020.
• [13] 이상선, 임상수, 이태헌, 성인영, 김선. Cancer subtype classification and modeling by pathway attention and propagation. AI KOREA 2019. 한국정보과학회 인공지능소사이어티. Jul. 26, 2019.
• [14] 강동원, 안홍렬, 이상선, 정우석, 김선. 시계열 유전자 발현 데이터의 스트레스 특이 반응 유전자 검출 및 스트레스 예측 알고리즘 개발. 한국정보과학회 2018 한국컴퓨터종합학술대회 논문집, Vol.45, pp.1063-1065. Jun., 2018.