기초과학연구원 의생명수학그룹
In this talk, we discuss the paper "N-BEATS: Neural basis expansion analysis for interpretable time series forecasting" by B. Oreshkin et al., ICLR, 2020. Abstract We focus on solving the …
Classical machine learning models are typically trained under the assumption that the training (source) and test (target) data are drawn from the same distribution. However, real-world data are rarely clean …
Recent advances in data science have expanded the scope of data analysis beyond prediction accuracy toward interpretability, causal understanding, and generalizable learning across complex data structures. This lecture introduces three …
Mathematical modeling provides essential quantitative insights that accelerate drug and cell therapy development. In this presentation, we utilize kinetic frameworks to optimize the design of molecular glues by elucidating their …
In this talk, we discuss the paper "Seasonal timing and interindividual differences in shiftwork adaptation" by R. Kim et al., npj digital medicine, 2025. Abstract Millions of shift workers in …
In this talk, we discuss the paper "scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction" by Z. Liang et al., arxiv, 2025. Abstract This paper introduces the Single-Cell Perturbation Prediction …
Complex diseases, such as cancer, sarcopenia, and immune disorders, arise from abnormalities in multiple genes and pathways, posing significant challenges to conventional single-target drug discovery strategies. To address this, we …
In this talk, we discuss the paper, "Quantifying interventional causality by knockoff operation" by Xinyan Zhang and Luonan Chen, Science Advances, 2025. Abstract Causal inference between measured variables is crucial …