데이터마이닝 및 인포메이션시스템 연구실

DMIS Lab focuses on Biomedical AI, NLP, Graph ML and other topics. Below are some exemplary works produced by lab members. More papers related to each of these topics can be found in the publications section. DMIS Lab leverages large-scale foundation models and multi-omics data integration to accelerate AI-driven drug discovery and target identification. Research focuses on precision medicine approaches, including patient stratification and personalized treatment response prediction using deep learning. DMIS Lab actively develops and applies large language models (LLMs) for biomedical and clinical NLP tasks, including medical question answering and clinical information extraction. Machine learning methods that are based on graph structures have gained much popularity as they provide utility for data in the form of graphs. As objects like drugs or chemicals may be represented by graphs of various forms, DMIS Lab focuses on develop machine learning algorithms that utilize such graphical structures. In addition to graph-based models, our lab also conducts research on more fundamental aspects of the field of ML in order to fine tune the effectiveness of our current tools and methods and enhance our understanding of the algorithms we use. DMIS Lab develops graph neural network (GNN) models for biomedical knowledge graphs, enabling tasks such as drug-target interaction prediction and disease network analysis. PI: Prof. Jaewoo Kang, Dept. of Computer Science and Engineering, Korea Univ. Data science has advanced to the point where it is changing our world. It is now the center of exploring and uncovering knowledge in different domains and acts as a bridge to connect them. With ever growing amount of data and opportunity to explore, the Data Mining and Information Systems (DMIS