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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260313T100000
DTEND;TZID=Asia/Seoul:20260313T233000
DTSTAMP:20260405T223705
CREATED:20260304T045936Z
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SUMMARY:A multimodal sleep foundation model for disease prediction - Jinwoo Hyun
DESCRIPTION:In this talk\, we discuss the paper “A multimodal sleep foundation model for disease prediction” by Rahul Thapa et al.\, Nature Medicine\, 2026. \nAbstract \nSleep is a fundamental biological process with broad implications for physical and mental health\, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich physiological signals but is underutilized due to challenges in standardization\, generalizability and multimodal integration. To address these challenges\, we developed SleepFM\, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG configurations. Trained on a curated dataset of over 585\,000 hours of PSG recordings from approximately 65\,000 participants across several cohorts\, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk. From one night of sleep\, SleepFM accurately predicts 130 conditions with a C-Index of at least 0.75 (Bonferroni-corrected P < 0.01)\, including all-cause mortality (C-Index\, 0.84)\, dementia (0.85)\, myocardial infarction (0.81)\, heart failure (0.80)\, chronic kidney disease (0.79)\, stroke (0.78) and atrial fibrillation (0.78). Moreover\, the model demonstrates strong transfer learning performance on a dataset from the Sleep Heart Health Study—a dataset that was excluded from pretraining—and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks\, achieving mean F1 scores of 0.70–0.78 for sleep staging and accuracies of 0.69 and 0.87 for classifying sleep apnea severity and presence. This work shows that foundation models can learn the language of sleep from multimodal sleep recordings\, enabling scalable\, label-efficient analysis and disease prediction.
URL:https://www.ibs.re.kr/bimag/event/a-multimodal-sleep-foundation-model-for-disease-prediction-jinwoo-hyun/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260320T100000
DTEND;TZID=Asia/Seoul:20260320T113000
DTSTAMP:20260405T223705
CREATED:20260304T051107Z
LAST-MODIFIED:20260304T051107Z
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SUMMARY:Temporal tissue dynamics from a spatial snapshot - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Temporal tissue dynamics from a spatial snapshot” by Jonathan Somer et al.\, Nature\, 2026. \nAbstract \nPhysiological and pathological processes such as inflammation and cancer emerge from interactions between cells over time1. However\, methods to follow cell populations over time within the native context of a human tissue are lacking because a biopsy offers only a single snapshot. Here we present one-shot tissue dynamics reconstruction (OSDR)\, an approach to estimate a dynamical model of cell populations based on a single tissue sample. OSDR uses spatial proteomics to learn how the composition of cellular neighbourhoods influences division rate\, providing a dynamical model of cell population change over time. We apply OSDR to human breast cancer data2\,3\,4\, and reconstruct two fixed points of fibroblasts and macrophage interactions5\,6. These fixed points correspond to hot and cold fibrosis7\, in agreement with co-culture experiments that measured these dynamics directly8. We then use OSDR to discover a pulse-generating excitable circuit of T and B cells in the tumour microenvironment\, suggesting temporal flares of anticancer immune responses. Finally\, we study longitudinal biopsies from a triple-negative breast cancer clinical trial3\, in which OSDR predicts the collapse of the tumour cell population in responders but not in non-responders\, based on early-treatment biopsies. OSDR can be applied to a wide range of spatial proteomics assays to enable analysis of tissue dynamics based on patient biopsies.
URL:https://www.ibs.re.kr/bimag/event/temporal-tissue-dynamics-from-a-spatial-snapshot-kang-min-lee/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260325T160000
DTEND;TZID=Asia/Seoul:20260325T170000
DTSTAMP:20260405T223705
CREATED:20260205T073506Z
LAST-MODIFIED:20260311T121525Z
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SUMMARY:Stochastic theory of complex biochemical reaction networks - Chen Jia
DESCRIPTION:Biochemical reaction networks and gene regulatory networks in cells are prototypical examples of complex systems\, characterized by highly nonlinear and stochastic\, multilevel dynamical interactions. Gaining a deep understanding of the stochastic dynamics and thermodynamic principles governing biochemical reaction networks not only helps elucidate the intrinsic mechanisms underlying cell fate decisions and the onset and progression of diseases\, but also provides new theoretical paradigms for the study of complex systems. This line of research has become one of the forefront interdisciplinary areas\, bridging mathematics\, physics\, biology\, chemistry\, statistics\, and intelligent science. In this talk\, I will present our recent research progress in this area\, with the hope of stimulating further discussion and inspiring new ideas. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/stochastic-theory-of-complex-biochemical-reaction-networks-chen-jia/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/02/Chen-Jia-e1770278480855.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260327T100000
DTEND;TZID=Asia/Seoul:20260327T113000
DTSTAMP:20260405T223705
CREATED:20260326T001251Z
LAST-MODIFIED:20260326T001251Z
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SUMMARY:A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games - Fanpeng Song
DESCRIPTION:In this talk\, we discuss the paper “A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games” by Qi Su et al.\, Nat. Comm.\, 2026. \nAbstract \nExploring dominant strategies in iterated games holds theoretical and practical significance across diverse domains. Previous studies\, through mathematical analysis of limited cases\, have unveiled classic strategies such as tit-for-tat\, generous-tit-for-tat\, win-stay-lose-shift\, and zero-determinant strategies. While these strategies offer valuable insights into human decision-making\, they represent only a small subset of possible strategies\, constrained by limited mathematical and computational tools available to explore larger strategy spaces. To bridge this gap\, we propose an approach using multi-agent reinforcement learning to delve into complex decision-making processes that go beyond human intuition. Our approach has led to the discovery of a strategy that we call memory-two bilateral reciprocity strategy. Memory-two bilateral reciprocity strategy consistently outperforms a wide range of strategies in pairwise interactions while achieving high payoffs. When introduced into an evolving population with diverse strategies\, memory-two bilateral reciprocity strategy demonstrates dominance and fosters higher levels of cooperation and social welfare in both homogeneous and heterogeneous structures\, as well as across various game types. This high performance is verified by simulations and mathematical analysis. Our work highlights the potential of multi-agent reinforcement learning in uncovering dominant strategies in iterated and evolutionary games.
URL:https://www.ibs.re.kr/bimag/event/a-multi-agent-reinforcement-learning-framework-for-exploring-dominant-strategies-in-iterated-and-evolutionary-games-fanpeng-song/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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