Subtype-specific CpG island shore methylation and mutation patterns in 30 breast cancer cell lines. - Heejoon Chae; Sangseon Lee; Kenneth P. Nephew; Sun Kim (BMC Syst. Biol., 2016)

Flow maximization analysis of cell cycle pathway activation status in breast cancer subtypes. - Sangseon Lee; Ji Hwan Moon; Youngjune Park; Sun Kim (BigComp, 2017)

PINTnet: construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI. - Ji Hwan Moon; Sangsoo Lim; Kyuri Jo; Sangseon Lee; Seokjun Seo; Sun Kim (BMC Syst. Biol., 2017)

Identifying stress-related genes and predicting stress types in Arabidopsis using logical correlation layer and CMCL loss through time-series data. - Dongwon Kang; Hongryul Ahn; Sangseon Lee; Chai-Jin Lee; Jihye Hur; Woosuk Jung; Sun Kim (BIBM, 2018)

PRISM: methylation pattern-based, reference-free inference of subclonal makeup. - Do-Hoon Lee; Sangseon Lee; Sun Kim (Bioinform., 2019)

Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments. - Benjamin Hur; Dongwon Kang; Sangseon Lee; Ji Hwan Moon; Gung Lee; Sun Kim (BMC Bioinform., 2019)

Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data. - Sangsoo Lim; Sangseon Lee; Inuk Jung; SungMin Rhee; Sun Kim (Briefings Bioinform., 2020)

Cancer subtype classification and modeling by pathway attention and propagation. - Sangseon Lee; Sangsoo Lim; Taeheon Lee; Inyoung Sung; Sun Kim (Bioinform., 2020)

A probabilistic model for pathway-guided gene set selection. - Inyoung Kim; Sangseon Lee; Youngkuk Kim; Hugh Namkoong; Sun Kim (BIBM, 2021)

Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG Islands. - Sangseon Lee; Taeheon Lee; Yung-Kyun Noh; Sun Kim (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification. - Yinhua Piao; Sangseon Lee; Dohoon Lee; Sun Kim (AAAI, 2022)

Embedding of FDA Approved Drugs in Chemical Space Using Cascade Autoencoder with Metric Learning. - Jungwoo Kim; Sangsoo Lim; Sangseon Lee; Changyun Cho; Sun Kim (BigComp, 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. - Inyoung Sung; Sangseon Lee; Minwoo Pak; Yunyol Shin; Sun Kim (BMC Bioinform., 2022)

SPGP: Structure Prototype Guided Graph Pooling. - Sangseon Lee; Dohoon Lee; Yinhua Piao; Sun Kim (CoRR, 2022)

MLDEG: A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network Propagation. - Ji Hwan Moon; Sangseon Lee; Minwoo Pak; Benjamin Hur; Sun Kim (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

Improved drug response prediction by drug target data integration via network-based profiling. - Minwoo Pak; Sangseon Lee; Inyoung Sung; Bonil Koo; Sun Kim (Briefings Bioinform., 2023)

A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning. - Jeonghyeon Gu; Dongmin Bang; Jungseob Yi; Sangseon Lee; Dong Kyu Kim; Sun Kim (Briefings Bioinform., 2023)

Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments. - Yinhua Piao; Sangseon Lee; Yijingxiu Lu; Sun Kim (CVPR, 2024)

CheapNet: Cross-attention on Hierarchical representations for Efficient protein-ligand binding Affinity Prediction. - Hyukjun Lim; Sun Kim; Sangseon Lee (ICLR, 2025)

Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target Information. - Sungjoon Park; Sangseon Lee; Minwoo Pak; Sun Kim (IEEE J. Biomed. Health Informatics, 2025)