기초과학연구원 의생명수학그룹
In this talk, we discuss the paper "Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology" by Ning Wei and Casey O Diekman, …
In this talk, we discuss the paper "Principled PCA separates signal from noise in omics count data" by Jay S. Stanley III et al., bioarxiv, 2025. Abstract Principal component analysis (PCA) …
본 발표는 삼성에서 개발 중인 디지털 헬스케어 기술을 소개합니다. 먼저, 삼성 갤럭시 웨어러블 센서의 기능과 활용 가능성을 설명하며, 웰니스 및 의료기기 서비스의 상품화 사례를 소개합니다. 또한, 삼성이 디지털 헬스케어를 통해 …
Digital health leverages information and communication technologies to transform healthcare, enabling diverse solutions for continuous health management. Among these, wearable-based digital health plays a key role by collecting, monitoring, and …
Abstract Recent emergence and re-emergence of infectious disease pathogens have caused major disruptions to our society, highlighting the importance of managing ongoing outbreaks and predicting future epidemics. In this talk, …
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This seminar examines how generative AI advances three foundational tasks in causality, treated as distinct, modular problems: (1) causal inference via intervention‑effect estimation, (2) causal graph analysis, and (3) detection …
In this talk, we discuss the paper "Modeling personalized heart rate response to exercise and environmental factors with wearables data" by Nazaret et al., npj digital medicine, 2023. Abstract Heart …
Abstract Many natural systems exhibit complex dynamics and are prone to sudden changes or ‘regime shifts’. At the same time, many of these systems are sparsely observed posing considerable challenges …
In this talk, we discuss the paper "Causal disentanglement for single-cell representations and controllable counterfactual generation" by Yicheng Gao et al., Nature Communications, 2025. Abstract Conducting disentanglement learning on single-cell …
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 …