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X-WR-CALDESC:Events for Biomedical Mathematics Group
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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Seoul:20260619T100000
DTEND;TZID=Asia/Seoul:20260619T120000
DTSTAMP:20260529T010455
CREATED:20260520T075146Z
LAST-MODIFIED:20260528T012253Z
UID:12428-1781863200-1781870400@www.ibs.re.kr
SUMMARY:Insulin resistance prediction from wearables and routine blood biomarkers - Dongju Lim
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. \n  \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/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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260626T100000
DTEND;TZID=Asia/Seoul:20260626T120000
DTSTAMP:20260529T010455
CREATED:20260528T012227Z
LAST-MODIFIED:20260528T012227Z
UID:12546-1782468000-1782475200@www.ibs.re.kr
SUMMARY:Learning Longitudinal Health Representations from EHR and Wearable Data - Hyunji Jeong
DESCRIPTION:In this talk\, we discuss the paper “Learning Longitudinal Health Representations from EHR and Wearable Data” by Yuanyun Zhang et al.\, arXiv\, 2026. \nAbstract: \nFoundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable devices provide dense continuous physiological signals but lack semantic grounding. Existing methods usually model these data sources separately or combine them through late fusion. We propose a multimodal foundation model that jointly represents electronic health records and wearable data as a continuous time latent process. The model uses modality specific encoders and a shared temporal backbone pretrained with self supervised and cross modal objectives. This design produces representations that are temporally coherent and clinically grounded. Across forecasting physiological and risk modeling tasks the model outperforms strong electronic health record only and wearable only baselines especially at long horizons and under missing data. These results show that joint electronic health record and wearable pretraining yields more faithful representations of longitudinal health.
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:20260703T100000
DTEND;TZID=Asia/Seoul:20260703T120000
DTSTAMP:20260529T010455
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
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