[Events]
[Prof. Lee Jee Hyong Lab Seminar] AI for Precision Medicine
소프트웨어학과
Hit301
2025-01-15
AI for Precision Medicine
▶ Abstract: AI is revolutionizing precision medicine by enabling advanced tools for disease understanding, diagnosis, and therapeutic innovation. Machine learning techniques such as graph-based learning uncover novel gene-disease associations, leveraging curated genomic databases like DisGeNET and HumanNet to accelerate biomarker discovery and targeted drug development. Frameworks like RIAS deliver reliable, interpretable predictions for critical applications, such as predicting mortality after acute myocardial infarction, addressing the “black-box” issue with local and global explanations and patient-specific counterfactual scenarios. Additionally, the AptaTrans pipeline introduces a transformer-based deep learning approach for aptamer-protein interaction (API) prediction, significantly enhancing the efficiency of SELEX-based drug discovery. By integrating structural representation and generative algorithms like Apta-MCTS, AptaTrans outperforms existing models, reducing time and cost in aptamer selection. Together, these advancements underscore AI's transformative potential in precision medicine, offering robust tools for targeted therapies and improved patient outcomes. ▶ Speaker: Giltae Song, Ph.D., Pusan National University Dr. Giltae Song is an associate professor in the School of Computer Science and Engineering at Pusan National University (PNU). Before joining PNU, he was a post-doctoral scholar in Prof. Mike Cherry's group at Stanford University. Dr. Song earned a Ph.D. in computer science and engineering from Pennsylvania State University (advised by Prof. Webb Miller), and both his bachelor's and master's degrees in computer science and engineering from Seoul National University. His research focuses on machine learning and data mining specialized for analyzing various biomedical data (e.g., genome sequence data, experimental data for drug discovery, and clinical data in hospitals). ▶ Date & Time: January 22, 11:00 AM (KST) ▶Location: Online
