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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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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260626T093000
DTEND;TZID=Asia/Seoul:20260626T113000
DTSTAMP:20260625T033224Z
CREATED:20260528T012227Z
LAST-MODIFIED:20260625T033224Z
UID:12546-1782466200-1782473400@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:20260615T110616Z
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:20260528T012333Z
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:20260616T012906Z
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
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