• Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning – Myna Lim

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    In this Journal club, we will discuss the paper "Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning", Weishen Pan et al., Nature Communications, 2025. Abstract: Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a significant challenge in practice. Unsupervised

  • Learning to learn ecosystems from limited data – Hyeong Jun Jang

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    In this talk, we discuss the paper "Learning to learn ecosystems from limited data" by Zheng-Meng Zhai et al., PNAS, 2025. Abstract: A fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurement data. For example, modern machine-learning techniques such