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PRODID:-//Biomedical Mathematics Group - ECPv6.16.3//NONSGML v1.0//EN
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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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X-Robots-Tag:noindex
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20250101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260527T160000
DTEND;TZID=Asia/Seoul:20260527T170000
DTSTAMP:20260617T092806
CREATED:20260523T013451Z
LAST-MODIFIED:20260523T023337Z
UID:12489-1779897600-1779901200@www.ibs.re.kr
SUMMARY:Causal Generalist Medical AI - Hongtu Zhu
DESCRIPTION:The rapid evolution of flexible and reusable artificial intelligence (AI) models is transforming medical science. This short course introduces Causal Generalist Medical AI (Causal GMAI)—a paradigm that integrates causal inference with generalist AI models to enhance interpretability\, robustness\, and generalizability in medical decision-making. Causal GMAI employs self-supervised\, semi-supervised\, and supervised learning on diverse multimodal datasets—including imaging\, electronic health records\, clinical trials\,  laboratory results\, genomics\, knowledge graphs\, and medical text—to perform a wide range of tasks with minimal task-specific supervision.  By embedding causal reasoning\, these models go beyond prediction to infer underlying causal relationships\, improving diagnostic accuracy\, treatment recommendations\, and personalized medicine. The course covers key technical components such as causal discovery\, counterfactual reasoning\, and domain adaptation\, alongside real-world applications.  We will also explore challenges in regulation\, validation\, and dataset curation to ensure clinical reliability and ethical deployment. Designed for researchers\, clinicians\, data scientists\, and AI practitioners\, this course provides a foundation for advancing the next generation of trustworthy and interpretable medical AI. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/hongtu-zhu-tba/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/05/hongtu.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260529T100000
DTEND;TZID=Asia/Seoul:20260529T120000
DTSTAMP:20260617T092806
CREATED:20260429T070610Z
LAST-MODIFIED:20260518T051101Z
UID:12398-1780048800-1780056000@www.ibs.re.kr
SUMMARY:Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies - Chitaranjan Mahapatra
DESCRIPTION:In this talk\, we discuss the paper “Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies” by Sam vidil et al.\, Nature Communications\, 2025. \nAbstract: \nAccelerometers allow objective measures of dimensions (rest-activity rhythm (RAR)\, daytime activity\, sleep\, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple metrics in large-scale studies. These dimensions are rarely examined together due to methodological challenges of using correlated data. To address this challenge\, we propose a two-step approach consisting of data reduction of CR metrics using principal component analyses\, followed by k-means clustering to identify groups of individuals with a similar profile using data from the Whitehall II (N = 3\,991\, mean age=69.4years) and UK Biobank (N = 54\,995\, mean age=67.5years) cohort studies. Our analyses identified nine CR clusters: two presented extreme (most robust/poorest) RAR and (highest/lowest) daytime activity\, two robust RAR with opposite sleep profiles (longer and efficient/shorter and fragmented)\, one high-intensity physical activity\, and four poor RAR (one characterised by late chronotype\, two by low activity but opposite sleep profiles\, and one by restless (agitated) sleep). The participants in these nine clusters differed on sociodemographic\, behavioural and health-related factors. Findings were similar in these two independent cohort studies\, highlighting the validity of our approach. Most previous studies have used only the RAR dimension of circadian rhythm\, and here we show that this might be an oversimplification as demonstrated by nine clusters characterised by combinations of RAR\, daytime activity\, sleep\, and chronotype. Our innovative approach demonstrates feasibility of using all dimensions to study the impact of circadian rhythm dysregulation on health.
URL:https://www.ibs.re.kr/bimag/event/circadian-rhythm-profiles-derived-from-accelerometer-measures-of-the-sleep-wake-cycle-in-two-cohort-studies-chitaranjan-mahapatra/
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:20260605T100000
DTEND;TZID=Asia/Seoul:20260605T120000
DTSTAMP:20260617T092806
CREATED:20260429T070216Z
LAST-MODIFIED:20260527T003414Z
UID:12396-1780653600-1780660800@www.ibs.re.kr
SUMMARY:Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction- Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction” by Yi He et al.\, ICML Poster\, 2025. \nAbstract: \nGenerating long-term trajectories of dissipative chaotic systems autoregressively is a highly challenging task. The inherent positive Lyapunov exponents amplify prediction errors over time. Many chaotic systems possess a crucial property — ergodicity on their attractors\, which makes long-term prediction possible. State-of-the-art methods address ergodicity by preserving statistical properties using optimal transport techniques. However\, these methods face scalability challenges due to the curse of dimensionality when matching distributions. To overcome this bottleneck\, we propose a scalable transformer-based framework capable of stably generating long-term high-dimensional and high-resolution chaotic dynamics while preserving ergodicity. Our method is grounded in a physical perspective\, revisiting the Von Neumann mean ergodic theorem to ensure the preservation of long-term statistics in the L2 space. We introduce novel modifications to the attention mechanism\, making the transformer architecture well-suited for learning large-scale chaotic systems. Compared to operator-based and transformer-based methods\, our model achieves better performances across five metrics\, from short-term prediction accuracy to long-term statistics. In addition to our methodological contributions\, we introduce new chaotic system benchmarks: a machine learning dataset of 140 snapshots of turbulent channel flow and a processed high-dimensional Kolmogorov Flow dataset\, along with various evaluation metrics for both short- and long-term performances. Both are well-suited for machine learning research on chaotic systems.
URL:https://www.ibs.re.kr/bimag/event/bridging-known-and-unknown-dynamics-by-transformer-based-machine-learning-inference-from-sparse-observations-gyuyoung-hwang/
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:20260619T100000
DTEND;TZID=Asia/Seoul:20260619T120000
DTSTAMP:20260617T092806
CREATED:20260520T075146Z
LAST-MODIFIED:20260614T040037Z
UID:12428-1781863200-1781870400@www.ibs.re.kr
SUMMARY:Inferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Inferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes” by Andrea Salati et al.\, bioRxiv\, 2026. \n  \nAbstract: \nSingle-cell RNA sequencing (scRNA-seq) reveals heterogeneity in circadian clock states across individual cells\, yet accurately inferring circadian phase and distinguishing biological desynchrony from technical noise remains challenging. Here\, we introduce scRitmo\, a probabilistic framework that infers single-cell circadian phases from mRNA count data\, providing both a point estimate and a posterior uncertainty for each cell. A simulationcalibrated variance decomposition separates the observed phase dispersion into biological and technical components\, enabling direct estimation of intercellular desynchrony. We validate scRitmo using deeply sequenced unsynchronized fibroblasts\, where inferred transcriptomic phases accurately predict protein-level oscillations of a circadian reporter. Applied to murine scRNA-seq datasets from liver\, aorta\, and skin\, scRitmo outperforms existing methods and reveals cell-type-specific levels of phase coherence. In SABER-FISH time-series data\, the method recovers the progressive accumulation of desynchrony following synchronization\, and in Drosophila clock neurons it captures cell-type-specific phase shifts and the expected increase in phase dispersion under constant darkness relative to light-dark entrainment. Together\, scRitmo provides a principled approach for quantifying circadian (de)synchrony from transcriptomic data\, decoupling biological phase variability from measurement noise across tissues\, organisms\, and experimental conditions.
URL:https://www.ibs.re.kr/bimag/event/insulin-resistance-prediction-from-wearables-and-routine-blood-biomarkers-dongju-lim/
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:20260626T100000
DTEND;TZID=Asia/Seoul:20260626T120000
DTSTAMP:20260617T092806
CREATED:20260528T012227Z
LAST-MODIFIED:20260614T035818Z
UID:12546-1782468000-1782475200@www.ibs.re.kr
SUMMARY:Insulin resistance prediction from wearables and routine blood biomarkers - Hyunji Jeong
DESCRIPTION:In this talk\, we discuss the paper “Insulin resistance prediction from wearables and routine blood biomarkers” by Ahmed A. Metwally et al.\, Nature\, 2026. \nAbstract: \nInsulin resistance (IR)\, a primary precursor to type 2 diabetes\, is characterized by impaired insulin action in tissues1. However\, diagnostic methods remain expensive and inaccessible\, which hinders early intervention2\,3. Here we present the WEAR-ME study\, a large\, remotely conducted study of IR (n = 1\,165 participants; median body mass index (BMI) = 28 kg m−2\, median age = 45 years\, median haemoglobin A1c (HbA1c) = 5.4%) that uses time-series data from wearable devices and routine blood biomarkers to train deep neural networks against a ground-truth measure of IR (homeostatic model assessment of IR; HOMA-IR). Using a HOMA-IR cut-off of 2.9\, our multimodal model achieved robust performance (area under the receiver operating characteristic curve (AUROC) = 0.80\, sensitivity = 76%\, specificity = 84%) with data from wearable devices\, together with demographic and routine blood biomarker data. To enhance the use of time-series data from wearables\, we fine-tuned a wearable foundation model (WFM) pretrained on 40 million hours of sensor data. In an independent validation cohort (n = 72)\, a model integrating WFM-derived representations with demographic data surpassed a demographics-only baseline (AUROC = 0.75 versus 0.66). Moreover\, adding WFM-derived representations to a model with demographics\, fasting glucose and a lipid panel substantially improved performance\, compared with an identical model without data from wearables (AUROC = 0.88 versus 0.76). We integrate IR prediction into a large language model to contextualize the results and facilitate personalized recommendations. This work establishes a scalable\, accessible framework for the early detection of metabolic risk\, which could enable timely lifestyle interventions to prevent progression to type 2 diabetes.
URL:https://www.ibs.re.kr/bimag/event/learning-longitudinal-health-representations-from-ehr-and-wearable-data-hyunji-jeong/
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:20260629T110000
DTEND;TZID=Asia/Seoul:20260629T120000
DTSTAMP:20260617T092806
CREATED:20260615T110616Z
LAST-MODIFIED:20260615T110616Z
UID:12608-1782730800-1782734400@www.ibs.re.kr
SUMMARY:Prediction of mood state change based on repeated functional brain imaging and mathematical modeling in premenstrual syndrome - Dayoung Yoon
DESCRIPTION:Abstract: \nAccurately predicting mood fluctuations in mood disorders is critical for early intervention and personalized treatment. This study developed a neurophysiologically grounded mood prediction model by integrating behavioral modeling\, electroencephalography\, functional magnetic resonance imaging (fMRI)\, and physiological data from wearable devices in premenstrual syndrome (PMS). First\, applying the active inference framework to a risk-taking behavioral task revealed that PMS is characterized by a significant reduction in policy precision during decision-making during the luteal phase. Rather than a failure in learning trajectories\, this reduction reflects impulsivity at the behavioral execution stage and closely correlates with a diminished amplitude of the contingent negative variation (CNV)—an event-related potential indicating pre-decision neural preparation. Second\, neural features extracted by applying cortical surface-based geometric eigenmodes to fMRI data successfully differentiated mood states in PMS. We confirmed that these neural features can be accounted for by the control energy required to maintain eigenmodes based on structural connectivity. Furthermore\, to overcome the cost and accessibility constraints of fMRI\, we constructed an encoder model that approximates fMRI-based latent brain states and predicts mood using only four circadian rhythm markers continuously collected from wearable devices. Finally\, the significant correlation between policy precision and the reduction in centro-parietal CNV amplitude was also significantly explained by the control energy of the eigenmodes. In conclusion\, this study presents a real-time\, personalized mood monitoring framework that is firmly grounded in neurobiological mechanisms yet practically applicable to daily life.
URL:https://www.ibs.re.kr/bimag/event/prediction-of-mood-state-change-based-on-repeated-functional-brain-imaging-and-mathematical-modeling-in-premenstrual-syndrome-dayoung-yoon/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260703T100000
DTEND;TZID=Asia/Seoul:20260703T120000
DTSTAMP:20260617T092806
CREATED:20260527T140141Z
LAST-MODIFIED:20260528T012333Z
UID:12539-1783072800-1783080000@www.ibs.re.kr
SUMMARY:A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations - Se Jun Ahn
DESCRIPTION:In this talk\, we discuss the paper “A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations” by Harkirat Sigh Arora et al.\, npj drug discovery\, 2026. \nAbstract: \nAntimicrobial resistance poses a major global threat\, driven by diminishing efficacy of current treatments and limited new therapies. Combination therapy with existing drugs offers a promising solution\, yet current empirical screening methods are expensive and often lead to suboptimal efficacy and inadvertent toxicity. We introduce CALMA\, a computational framework that quantitatively analyzes the potency-toxicity landscape of multi-drug combinations. Integrating genome-scale metabolic modeling with a neural network that reflects metabolic subsystems\, CALMA enhances interpretability and prioritizes pathways influencing drug interactions. The incorporation of metabolic architecture in the neural network leads to over 92% reduction in model parameters\, enabling it to learn generalizable mechanistic signals and reducing the experimental search space of optimal combinations by 97%. CALMA identified promising antimicrobial combinations against Escherichia coli and Mycobacterium tuberculosis that were antagonistic for kidney and liver toxicity and uncovered the nucleotide salvage pathway as a selective influencer of toxicity\, which was validated in vitro. Mining of health records of over 400\,000 patients showed reduced frequency of kidney side-effects in patients taking a vancomycin combination identified by CALMA. CALMA provides a rational\, mechanistic approach to streamline combination treatment design.
URL:https://www.ibs.re.kr/bimag/event/a-metabolism-informed-neural-network-identifies-pathways-influencing-the-potency-and-toxicity-of-antimicrobial-combinations-se-jun-ahn/
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:20260706T110000
DTEND;TZID=Asia/Seoul:20260706T120000
DTSTAMP:20260617T092806
CREATED:20260616T012906Z
LAST-MODIFIED:20260616T012906Z
UID:12611-1783335600-1783339200@www.ibs.re.kr
SUMMARY:The effect of the fitness gradient - Jakub Svoboda
DESCRIPTION:Abstract: \nEvolutionary biology studies populations of reproducing individuals and how their composition changes over time.An important question is the fixation probability of a single mutant that attempts to invade a homogeneous population.Many real populations experience gradients of chemicals or nutrients that cause mutations to be beneficial in some spatial regions and harmful in others.We will examine the fixation probability of a mutant placed on a simple one-dimensional spatial structure that experiences such a gradient.The mutant’s fitness varies linearly but is on average 1\, whereas the resident’s fitness is constant and equal to 1.We will prove nonintuitive results about the fixation probability of mutants.
URL:https://www.ibs.re.kr/bimag/event/the-effect-of-the-fitness-gradient-jakub-svoboda/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
END:VEVENT
END:VCALENDAR