BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.17.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260508T100000
DTEND;TZID=Asia/Seoul:20260508T120000
DTSTAMP:20260506T033947Z
CREATED:20260406T041825Z
LAST-MODIFIED:20260506T033947Z
UID:12360-1778234400-1778241600@www.ibs.re.kr
SUMMARY:Impact of daylight saving time on physical activity patterns - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “Impact of daylight saving time on physical activity patterns” by Hayoung Jeong et al.\, Nature Health\, 2026. \nAbstract\nDaylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits\, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures from the National Institutes of Health All of Us Research Program. Avoiding strict modelling assumptions\, we used a natural difference-in-differences design with Arizona (no DST) as a control against neighbouring Mountain Time states (observing DST). Contrary to common belief\, DST transitions produced no net change in total daily steps. Instead\, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (confidence interval = [78\, 326]\, P = 0.001) while reducing evening steps by 180 (confidence interval = [−263\, −97]\, P < 0.001); spring transitions showed the opposite. Importantly\, these treatment effects varied by demographics and across data-driven activity phenotypes (‘morning walker’\, ‘neutral walker’ and ‘evening walker’). These disparities suggest that structural factors (for example\, rigid work schedules\, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically\, resting heart rate showed subtle intraday shifts mirroring behavioural changes\, although differences were clinically insignificant. Our study provides a large-scale causal analysis of DST’s influence using continuous wearables data\, illustrating how observational data can generate real-world evidence to inform health-relevant policies.
URL:https://www.ibs.re.kr/bimag/event/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-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:20260424T100000
DTEND;TZID=Asia/Seoul:20260424T120000
DTSTAMP:20260406T103749Z
CREATED:20260406T092550Z
LAST-MODIFIED:20260406T103749Z
UID:12365-1777024800-1777032000@www.ibs.re.kr
SUMMARY:Foundation Models for Wearable Movement Data in Mental Health Research - Aqsa Awan
DESCRIPTION:In this tallk\, we discuss the paper “Foundation Models for Wearable Movement Data in Mental Health Research” by Franklin Y. Ruan et al.\, arXiv\, 2025. \nAbstract \nPretrained foundation models and transformer architectures have driven the success of large language models (LLMs) and other modern AI breakthroughs. However\, similar advancements in health data modeling remain limited due to the need for innovative adaptations. Wearable movement data offers a valuable avenue for exploration\, as it’s a core feature in nearly all commercial smartwatches\, well established in clinical and mental health research\, and the sequential nature of the data shares similarities to language. We introduce the Pretrained Actigraphy Transformer (PAT)\, the first open source foundation model designed for time-series wearable movement data. Leveraging transformer-based architectures and novel techniques\, such as patch embeddings\, and pretraining on data from 29\,307 participants in a national U.S. sample\, PAT achieves state-of-the-art performance in several mental health prediction tasks. PAT is also lightweight and easily interpretable\, making it a robust tool for mental health research.
URL:https://www.ibs.re.kr/bimag/event/foundation-models-for-wearable-movement-data-in-mental-health-research-aqsa-awan/
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:20260417T100000
DTEND;TZID=Asia/Seoul:20260417T120000
DTSTAMP:20260406T060603Z
CREATED:20260403T080037Z
LAST-MODIFIED:20260406T060603Z
UID:12336-1776420000-1776427200@www.ibs.re.kr
SUMMARY:Discovering network dynamics with neural symbolic regression - Olive Cawiding
DESCRIPTION:In this tallk\, we discuss the paper “Discovering network dynamics with neural symbolic regression” by Zihan Yu et al.\, Nature Com. Science\, 2026. \nAbstract  \nNetwork dynamics are fundamental to analyzing the properties of high-dimensional complex systems and understanding their behavior. Despite the accumulation of observational data across many domains\, mathematical models exist in only a few areas with clear underlying principles. Here we show that a neural symbolic regression approach can bridge this gap by automatically deriving formulas from data. Our method reduces searches on high-dimensional networks to equivalent one-dimensional systems and uses pretrained neural networks to guide accurate formula discovery. Applied to ten benchmark systems\, it recovers the correct forms and parameters of underlying dynamics. In two empirical natural systems\, it corrects existing models of gene regulation and microbial communities\, reducing prediction error by 59.98% and 55.94%\, respectively. In epidemic transmission across human mobility networks of various scales\, it discovers dynamics that exhibit the same power-law distribution of node correlations across scales and reveal country-level differences in intervention effects. These results demonstrate that machine-driven discovery of network dynamics can enhance understandings of complex systems and advance the development of complexity science.
URL:https://www.ibs.re.kr/bimag/event/discovering-network-dynamics-with-neural-symbolic-regression-olive-cawiding/
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:20260327T100000
DTEND;TZID=Asia/Seoul:20260327T113000
DTSTAMP:20260326T001251Z
CREATED:20260326T001251Z
LAST-MODIFIED:20260326T001251Z
UID:12316-1774605600-1774611000@www.ibs.re.kr
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260320T100000
DTEND;TZID=Asia/Seoul:20260320T113000
DTSTAMP:20260304T051107Z
CREATED:20260304T051107Z
LAST-MODIFIED:20260304T051107Z
UID:12253-1774000800-1774006200@www.ibs.re.kr
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:20260313T100000
DTEND;TZID=Asia/Seoul:20260313T233000
DTSTAMP:20260304T045936Z
CREATED:20260304T045936Z
LAST-MODIFIED:20260304T045936Z
UID:12251-1773396000-1773444600@www.ibs.re.kr
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260227T100000
DTEND;TZID=Asia/Seoul:20260227T113000
DTSTAMP:20260219T012250Z
CREATED:20260219T012250Z
LAST-MODIFIED:20260219T012250Z
UID:12219-1772186400-1772191800@www.ibs.re.kr
SUMMARY:TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation” by J.-B. Morlot et al.\, bioarxiv\, 2026. \nAbstract \nDrug discovery is impeded by the difficulty of translating targets from preclinical models to patients. Here\, we present TwinCell\, a Large Causal Cell Model (LCCM) capable of generalising from in vitro cell lines to patient-derived cell types while providing biologically meaningful interpretations of its predictions. Rather than predicting perturbation outcomes\, TwinCell focuses on identifying the upstream regulators driving the transition between diseased and healthy states. By integrating single-cell foundation model embeddings with a multiomics interactome\, TwinCell constrains predictions to mechanistically plausible signalling routes. To validate this approach\, we introduce TwinBench\, a novel benchmarking framework to assess virtual cell models generalisation capability while correcting for popularity bias and mode collapse through empirical p-value estimation. When applied to in clinico data\, TwinCell recovers known drug targets aligned with the biology of the disease and demonstrates generalisation performance on targets unseen during training. This highlights the model’s ability to learn mechanistic principles in a biological context. TwinCell represents a significant step toward building reliable and interpretable virtual cells for target identification bridging the gap between high-throughput in vitro experiments and clinical insights.
URL:https://www.ibs.re.kr/bimag/event/twincell-large-causal-cell-model-for-reliable-and-interpretable-therapeutic-target-prioritisation-yun-min-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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260220T100000
DTEND;TZID=Asia/Seoul:20260220T113000
DTSTAMP:20260219T012056Z
CREATED:20260203T014207Z
LAST-MODIFIED:20260219T012056Z
UID:12168-1771581600-1771587000@www.ibs.re.kr
SUMMARY:GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design” by M. Filo et al.\, arxiv\, 2026. \nAbstract \nBiomolecular networks underpin emerging technologies in synthetic biology—from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics—and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation\, the reverse problem of discovering a network from a behavioral specification is difficult\, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net\, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel\, topologically diverse solutions across multiple design tasks\, in- cluding dose responses\, complex logic gates\, classifiers\, oscillators\, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs\, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.
URL:https://www.ibs.re.kr/bimag/event/quantum-inspired-approach-to-analyzing-complex-system-dynamics-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:20260213T100000
DTEND;TZID=Asia/Seoul:20260213T113000
DTSTAMP:20260203T013928Z
CREATED:20260203T013928Z
LAST-MODIFIED:20260203T013928Z
UID:12166-1770976800-1770982200@www.ibs.re.kr
SUMMARY:Intelligent in-cell electrophysiology - Chitaranjan Mahapatra
DESCRIPTION:In this talk\, we discuss the paper “Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings” by K. Rahmani et al.\, Nat. Comm\, 2025. \nAbstract \nIntracellular electrophysiology is essential in neuroscience\, cardiology\, and pharmacology for studying cells’ electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer a promising alternative by enabling simultaneous intracellular and extracellular action potential (iAP and eAP) recordings with high throughput. However\, accessing intracellular potentials with NEAs remains challenging. This study presents an AI-supported technique that leverages thousands of synchronous eAP and iAP pairs from stem-cell-derived cardiomyocytes on NEAs. Our analysis revealed strong correlations between specific eAP and iAP features\, such as amplitude and spiking velocity\, indicating that extracellular signals could be reliable indicators of intracellular activity. We developed a physics-informed deep learning model to reconstruct iAP waveforms from extracellular recordings recorded from NEAs and Microelectrode arrays (MEAs)\, demonstrating its potential for non-invasive\, long-term\, high-throughput drug cardiotoxicity assessments. This AI-based model paves the way for future electrophysiology research across various cell types and drug interactions.
URL:https://www.ibs.re.kr/bimag/event/intelligent-in-cell-electrophysiology-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:20260206T100000
DTEND;TZID=Asia/Seoul:20260206T113000
DTSTAMP:20260203T013529Z
CREATED:20260203T013529Z
LAST-MODIFIED:20260203T013529Z
UID:12164-1770372000-1770377400@www.ibs.re.kr
SUMMARY:Multi-Marginal Flow Matching with Adversarially Learnt Interpolants - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Multi-Marginal Flow Matching with Adversarially Learnt Interpolants” by O. Kviman et al.\, 2025\, arxiv. \nAbstract \nLearning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajectories are available\, but one has only snapshots of data taken at discrete time steps\, the problem of modelling the dynamics\, and thus inferring the underlying trajectories\, can be solved by multi-marginal generalisations of flow matching algorithms. This paper proposes a novel flow matching method that overcomes the limitations of existing multi-marginal trajectory inference algorithms. Our proposed method\, ALI-CFM\, uses a GAN-inspired adversarial loss to fit neurally parametrised interpolant curves between source and target points such that the marginal distributions at intermediate time points are close to the observed distributions. The resulting interpolants are smooth trajectories that\, as we show\, are unique under mild assumptions. These interpolants are subsequently marginalised by a flow matching algorithm\, yielding a trained vector field for the underlying dynamics. We showcase the versatility and scalability of our method by outperforming the existing baselines on spatial transcriptomics and cell tracking datasets\, while performing on par with them on single-cell trajectory prediction.
URL:https://www.ibs.re.kr/bimag/event/multi-marginal-flow-matching-with-adversarially-learnt-interpolants-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:20260130T100000
DTEND;TZID=Asia/Seoul:20260130T120000
DTSTAMP:20260116T013852Z
CREATED:20260116T013852Z
LAST-MODIFIED:20260116T013852Z
UID:12138-1769767200-1769774400@www.ibs.re.kr
SUMMARY:Generic Temperature Response of Large Biochemical Networks - Shingo Gibo
DESCRIPTION:In this talk\, we discuss the paper “Generic Temperature Response of Large Biochemical Networks” by Julian B. Voits and Ulrich S. Schwarz\, PRX Life\, 2025. \nAbstract  \nBiological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever\, when a modest rise in body temperature of only few Kelvin has strong effects on our immune system and how it fights pathogens. Another very important example is climate change\, when even smaller temperature changes lead to dramatic shifts in ecosystems. Although it is generally accepted that the main effect of an increase in temperature is the acceleration of biochemical reactions according to the Arrhenius equation\, it is not clear how it affects large biochemical networks with complicated architectures. For developmental systems such as fly and frog\, it has been shown that the system response to temperature deviates in a characteristic manner from the linear Arrhenius plot of single reactions\, but a rigorous explanation has not been given yet. Here we use a graph-theoretical interpretation of the mean first-passage times of a biochemical master equation to give a statistical description. We find that in the limit of large system size and if the network has a bias towards a target state\, then the Arrhenius plot is generically quadratic\, in excellent agreement with numerical simulations for large networks as well as with experimental data for developmental times in fly. We also discuss under which conditions this generic response can be violated\, for example for linear chains\, which have only one spanning tree.
URL:https://www.ibs.re.kr/bimag/event/generic-temperature-response-of-large-biochemical-networks-shingo-gibo/
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:20260123T100000
DTEND;TZID=Asia/Seoul:20260123T120000
DTSTAMP:20260119T002842Z
CREATED:20260116T013712Z
LAST-MODIFIED:20260119T002842Z
UID:12136-1769162400-1769169600@www.ibs.re.kr
SUMMARY:A wearable-based aging clock associates with disease and behavior - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper\, “A wearable-based aging clock associates with disease and behavior” by A. C. Miller et al.\, Nature Comm\, 2025. \nAbstract  \nAging biomarkers play a vital role in understanding longevity\, with the potential to improve clinical decisions and interventions. Existing aging clocks typically use blood\, vitals\, or imaging collected in a clinical setting. Wearables\, in contrast\, can make frequent and inexpensive measurements throughout daily living. Here we develop PpgAge\, an aging clock using photoplethysmography at the wrist from a consumer wearable. Using the Apple Heart & Movement Study (n = 213\,593 participants; >149 million participant-days)\, our observational analysis shows that this non-invasive and passively collected aging clock accurately predicts chronological age and captures signs of healthy aging. Participants with an elevated PpgAge gap (i.e.\, predicted age greater than chronological age) have significantly higher diagnosis rates of heart disease\, heart failure\, and diabetes. Elevated PpgAge gap is also a significant predictor of incident heart disease events (and new diagnoses) when controlling for relevant risk factors. PpgAge also associates with behavior\, including smoking\, exercise\, and sleep. Longitudinally\, PpgAge exhibits a sharp increase during pregnancy and concurrent with certain types of cardiac events.
URL:https://www.ibs.re.kr/bimag/event/mapping-the-genetic-landscape-across-14-psychiatric-disorders-myna-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:20260115T100000
DTEND;TZID=Asia/Seoul:20260115T113000
DTSTAMP:20260109T124602Z
CREATED:20260109T124602Z
LAST-MODIFIED:20260109T124602Z
UID:12099-1768471200-1768476600@www.ibs.re.kr
SUMMARY:Quantifying interventional causality by knockoff operation - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper\, “Quantifying interventional causality by knockoff operation” by Xinyan Zhang and Luonan Chen\, Science Advances\, 2025. \nAbstract  \nCausal inference between measured variables is crucial to understand the underlying mechanism of complex biological processes at a network level but remains challenging in computational biology. We propose an innovative causal criterion\, knockoff conditional mutual information (KOCMI)\, to accurately infer interventional direct causality without prior knowledge of the network structure using either time-independent or time-series data. KOCMI performs knockoff operation on a variable as its virtual intervention\, which preserves the original network structure\, and then identifies the causality between two variables by estimating the distributional invariance before and after such a virtual intervention. We show that\, algorithmically\, KOCMI enables quantification of causal relationship\, even for networks with loops\, and\, theoretically\, is also consistent with the do-calculus causal analyses but without their prerequisite of the network structure. KOCMI shows superior performance on benchmark and real datasets\, comparing with existing methods. Overall\, KOCMI provides a powerful tool in inferring interventional causality\, which is theoretically ensured and experimentally validated by real intervention data.
URL:https://www.ibs.re.kr/bimag/event/quantifying-interventional-causality-by-knockoff-operation-olive-cawiding/
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:20260109T100000
DTEND;TZID=Asia/Seoul:20260109T113000
DTSTAMP:20251231T002857Z
CREATED:20251231T002857Z
LAST-MODIFIED:20251231T002857Z
UID:12078-1767952800-1767958200@www.ibs.re.kr
SUMMARY:scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction” by Z. Liang et al.\, arxiv\, 2025. \nAbstract \nThis paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM)\, the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels\, pre-perturbation state and drug with dose\, in a unified latent space via non-concatenative GD-Attn. During inference\, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude for tunable intensity. Evaluated on the Tahoe-100M benchmark under two stringent regimes\, unseen covariate combinations (UC) and unseen drugs (UD)\, scPPDM sets new state-of-the-art results across log fold-change recovery\, delta correlations\, explained variance\, and DE-overlap. Representative gains include +36.11%/+34.21% on DEG logFC-Spearman/Pearson in UD over the second-best model. This control interface enables transparent what-if analyses and dose tuning\, reducing experimental burden while preserving biological specificity.
URL:https://www.ibs.re.kr/bimag/event/scppdm-a-diffusion-model-for-single-cell-drug-response-prediction-aqsa-awan/
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:20260102T100000
DTEND;TZID=Asia/Seoul:20260102T113000
DTSTAMP:20251231T002614Z
CREATED:20251231T002614Z
LAST-MODIFIED:20251231T002614Z
UID:12076-1767348000-1767353400@www.ibs.re.kr
SUMMARY:Seasonal timing and interindividual differences in shiftwork adaptation - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Seasonal timing and interindividual differences in shiftwork adaptation” by R. Kim et al.\, npj digital medicine\, 2025. \nAbstract  \nMillions of shift workers in the U.S. face an increased risk of depression\, cancer\, and metabolic disease\, yet individual responses to shift work vary widely. We find that a conserved biological system of morning and evening oscillators\, which evolved for seasonal timing\, may contribute to these interindividual differences. In this study\, we analyze seasonality in medical interns working shifts\, revealing that summer-winter variation correlates with increased circadian misalignment after shift work. Mathematical modeling suggests that seasonal timing influences the rate of adaptation to new schedules\, predicting differential effects on morning and evening oscillators. Additionally\, we examine genetic polymorphisms linked to seasonality in animals and find that human variants can impact how quickly circadian rhythms respond to schedule changes. Based on our findings\, we hypothesize that the vast interindividual differences in shift work adaptation—critical for shift worker health—can in part be explained by biological mechanisms for seasonal timing.
URL:https://www.ibs.re.kr/bimag/event/seasonal-timing-and-interindividual-differences-in-shiftwork-adaptation-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:20251226T100000
DTEND;TZID=Asia/Seoul:20251226T120000
DTSTAMP:20251226T002150Z
CREATED:20251026T141349Z
LAST-MODIFIED:20251226T002150Z
UID:11798-1766743200-1766750400@www.ibs.re.kr
SUMMARY:N-BEATS: Neural basis expansion analysis for interpretable time series forecasting - Jinwoo Hyun
DESCRIPTION: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. \nAbstract \nWe focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties\, being interpretable\, applicable without modification to a wide array of target domains\, and fast to train. We test the proposed architecture on several well-known datasets\, including M3\, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets\, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year’s winner of the M4 competition\, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that\, contrarily to received wisdom\, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally\, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.
URL:https://www.ibs.re.kr/bimag/event/journal-club-jinwoo-hyun/
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:20251212T110000
DTEND;TZID=Asia/Seoul:20251212T123000
DTSTAMP:20251215T043112Z
CREATED:20251026T141250Z
LAST-MODIFIED:20251215T043112Z
UID:11796-1765537200-1765542600@www.ibs.re.kr
SUMMARY:Quantifying interventional causality by knockoff operation - Yun Min Song
DESCRIPTION: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. \nAbstract  \nConducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell\, which incorporates the factual information about causal relationships among disentangled concepts within a diffusion model to generate more reliable disentangled cellular representations\, with the aim of increasing the explainability\, generalizability and controllability of single-cell data\, including spatial-temporal omics data\, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios\, i.e.\, disentanglement and reconstruction\, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark\, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally\, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets.
URL:https://www.ibs.re.kr/bimag/event/journal-club-yun-min-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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251121T100000
DTEND;TZID=Asia/Seoul:20251121T120000
DTSTAMP:20251121T000249Z
CREATED:20251026T141157Z
LAST-MODIFIED:20251121T000249Z
UID:11794-1763719200-1763726400@www.ibs.re.kr
SUMMARY:Modeling personalized heart rate response to exercise and environmental factors with wearables data - Dongju Lim
DESCRIPTION: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. \nAbstract \nHeart rate (HR) response to workout intensity re ects tness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory tness. However\, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with exible neural network components to learn a personalized\, multidimensional representation of tness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors\, from area topography to weather and instantaneous workout\nintensity. Our approach ef ciently ts the hybrid model to a large set of 270\,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces tness representations that accurately predict full HR response to exercise intensity in future workouts\, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory tness\, such as VO2 max (explained variance\n0.81 ± 0.003). Lastly\, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate\, e.g.\, high temperatures can increase heart rate by 10%. Combining physiological ODEs with exible neural networks can yield interpretable\, robust\, and expressive models for health applications.
URL:https://www.ibs.re.kr/bimag/event/journal-club-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:20251107T100000
DTEND;TZID=Asia/Seoul:20251107T120000
DTSTAMP:20251107T000601Z
CREATED:20251026T141100Z
LAST-MODIFIED:20251107T000601Z
UID:11791-1762509600-1762516800@www.ibs.re.kr
SUMMARY:Principled PCA separates signal from noise in omics count data - Hyun Kim
DESCRIPTION: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.  \nAbstract \nPrincipal component analysis (PCA) is indispensable for processing high-throughput omics datasets\, as it can extract meaningful biological variability while minimizing the influence of noise. However\, the suitability of PCA is contingent on appropriate normalization and transformation of count data\, and accurate selection of the number of principal components; improper choices can result in the loss of biological information or corruption of the signal due to excessive noise. Typical approaches to these challenges rely on heuristics that lack theoretical foundations. In this work\, we present Biwhitened PCA (BiPCA)\, a theoretically grounded framework for rank estimation and data denoising across a wide range of omics modalities. BiPCA overcomes a fundamental difficulty with handling count noise in omics data by adaptively rescaling the rows and columns – a rigorous procedure that standardizes the noise variances across both dimensions. Through simulations and analysis of over 100 datasets spanning seven omics modalities\, we demonstrate that BiPCA reliably recovers the data rank and enhances the biological interpretability of count data. In particular\, BiPCA enhances marker gene expression\, preserves cell neighborhoods\, and mitigates batch effects. Our results establish BiPCA as a robust and versatile framework for high-throughput count data analysis.
URL:https://www.ibs.re.kr/bimag/event/from-noise-to-models-to-numbers-evaluating-negative-binomial-models-and-parameter-estimations-in-single-cell-rna-seq-hyun-kim/
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:20251031T090000
DTEND;TZID=Asia/Seoul:20251031T103000
DTSTAMP:20251023T035154Z
CREATED:20251004T064151Z
LAST-MODIFIED:20251023T035154Z
UID:11754-1761901200-1761906600@www.ibs.re.kr
SUMMARY:Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology - Chitaranjan Mahapatra
DESCRIPTION: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\, J. Biol. Rhythms\, 2025.  \nAbstract \nCircadian clocks regulate many aspects of human physiology\, including cardiovascular function and drug metabolism. Administering drugs at optimal times of the day may enhance effectiveness and reduce side effects. Certain cardiac antiarrhythmic drugs have been withdrawn from the market due to unexpected proarrhythmic effects such as fatal Torsade de Pointes (TdP) ventricular tachycardia. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a recent global initiative to create guidelines for the assessment of drug-induced arrhythmias that recommends a central role for computational modeling of ion channels and in silico evaluation of compounds for TdP risk. We simulated circadian regulation of cardiac excitability and explored how dosing time of day affects TdP risk for 11 drugs previously classified into risk categories by CiPA. The model predicts that a high-risk drug taken at the most optimal time of day may actually be safer than a low-risk drug taken at the least optimal time of day. Based on these proof-of-concept results\, we advocate for the incorporation of circadian clock modeling into the CiPA paradigm for assessing drug-induced TdP risk. Since cardiotoxicity is the leading cause of drug discontinuation\, modeling cardiac-related chronopharmacology has significant potential to improve therapeutic outcomes.
URL:https://www.ibs.re.kr/bimag/event/dosing-time-of-day-impacts-the-safety-of-antiarrhythmic-drugs-in-a-computational-model-of-cardiac-electrophysiology-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:20251024T100000
DTEND;TZID=Asia/Seoul:20251024T120000
DTSTAMP:20251023T035250Z
CREATED:20250928T144047Z
LAST-MODIFIED:20251023T035250Z
UID:11629-1761300000-1761307200@www.ibs.re.kr
SUMMARY:Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks” by Fernando L. Metz\, Phys. Rev. Letters\, 2025. \nAbstract \nAlthough real-world complex systems typically interact through sparse and heterogeneous networks\, analytic solutions of their dynamics are limited to models with all-to-all interactions. Here\, we solve the dynamics of a broad range of nonlinear models of complex systems on sparse directed networks with a random structure. By generalizing dynamical mean-field theory to sparse systems\, we derive an exact equation for the path probability describing the effective dynamics of a single degree of freedom. Our general solution applies to key models in the study of neural networks\, ecosystems\, epidemic spreading\, and synchronization. Using the population dynamics algorithm\, we solve the path-probability equation to determine the phase diagram of a seminal neural network model in the sparse regime\, showing that this model undergoes a transition from a fixed-point phase to chaos as a function of the network topology.
URL:https://www.ibs.re.kr/bimag/event/dynamical-mean-field-theory-of-complex-systems-on-sparse-directed-networks-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:20251017T100000
DTEND;TZID=Asia/Seoul:20251017T120000
DTSTAMP:20251004T064334Z
CREATED:20250928T143709Z
LAST-MODIFIED:20251004T064334Z
UID:11627-1760695200-1760702400@www.ibs.re.kr
SUMMARY:Simulating the Spread of Infection in Networks with Quantum Computers - Shingo Gibo
DESCRIPTION:In this talk\, we discuss the paper “Simulating the Spread of Infection in Networks with Quantum Computers” by Xiaoyang Wang\, Yinchenguang Lyu\, Changyu Yao and Xiao Yuan\, Physical Review Applied\, vol.19\, 064035 (2023). \nAbstract \nWe propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution and spin-lattice configurations with Ising-type interactions. Then\, since the spreading process can be modeled as a classical Markovian process\, we show that the spreading process can be simulated using the evolution of a quantum thermal dynamic model with a parameterized Hamiltonian. In particular\, we analytically and numerically analyze the evolution behavior of the Hamiltonian\, and prove that the evolution simulates a classical Markovian process\, which describes the well-known epidemiological stochastic susceptible and infectious (SI) model. A practical method to determine the parameters of the thermal dynamic Hamiltonian from epidemiological inputs is exhibited. As an example\, we simulate the infection spreading process of the SARS-Cov-2 variant Omicron in a small-world network.
URL:https://www.ibs.re.kr/bimag/event/simulating-the-spread-of-infection-in-networks-with-quantum-computers-shingo-gibo/
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:20250926T100000
DTEND;TZID=Asia/Seoul:20250926T113000
DTSTAMP:20250924T001304Z
CREATED:20250915T083322Z
LAST-MODIFIED:20250924T001304Z
UID:11589-1758880800-1758886200@www.ibs.re.kr
SUMMARY:Tackling inter-subject variability in smartwatch data using factorization models - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “Tackling inter-subject variability in smartwatch data using factorization models” by Arman Naseri et. al\, Scientific Reports\, 2025. \nAbstract \nSmartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However\, differences among subjects (inter-subject variability) limit a model to generalize to unseen subjects. This study focused on binary classification tasks using heart rate and step counter from smartwatches\, including night/day and inactive/active classification\, as well as sleep and SpO2-related (oxygen saturation) tasks. To address inter-subject variability\, we explored different transforming and normalization regimes for time series including per-subject and population-based strategies. We propose a modified factorized autoencoder\, which separates the data into two latent spaces capturing class-specific and subject-specific information. Our proposed generalized factorized autoencoder and triplet factorized autoencoder improved classification accuracy over the baseline from 74.8 (± 10.5) to 83.1 (± 5.1) and 83.4 (± 5.3)\, respectively\, for night/day classification\, gains for inactive/active classification were modest\, improving from 84.3 (± 9.4) to 86.9 (± 4.4) and 86.6 (± 4.3)\, respectively. Our study highlights challenges of handling inter-subject variability in smartwatch data and how factorization models can be used to enable more robust and personalized health monitoring solutions for diverse populations.
URL:https://www.ibs.re.kr/bimag/event/tackling-inter-subject-variability-in-smartwatch-data-using-factorization-models-myna-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:20250919T160000
DTEND;TZID=Asia/Seoul:20250919T180000
DTSTAMP:20250901T020244Z
CREATED:20250825T081133Z
LAST-MODIFIED:20250901T020244Z
UID:11435-1758297600-1758304800@www.ibs.re.kr
SUMMARY:SCassist: An AI Based Workflow Assistant for Single-Cell Analysis - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “SCassist: An AI Based Workflow Assistant for Single-Cell Analysis ” by Vijayaraj Nagarajan et al.\, bioarxiv\, 2025.  \nAbstract \nSingle-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow\, requiring significant expertise and time. To navigate this complexity\, we have developed SCassist\, an R package that leverages the power of the large language models (LLM’s) to guide and enhance scRNA-seq analysis. SCassist integrates LLM’s into key workflow steps\, to analyze user data and provide relevant recommendations for filtering\, normalization and clustering parameters. It also provides LLM guided insightful interpretations of variable features and principal components\, along with cell type annotations and enrichment analysis. SCassist provides intelligent assistance using popular LLM’s like Google’s Gemini\, OpenAI’s GPT and Meta’s Llama3\, making scRNA-seq analysis accessible to researchers at all levels.
URL:https://www.ibs.re.kr/bimag/event/scassist-an-ai-based-workflow-assistant-for-single-cell-analysis-aqsa-awan/
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:20250912T140000
DTEND;TZID=Asia/Seoul:20250912T160000
DTSTAMP:20250910T002342Z
CREATED:20250825T081619Z
LAST-MODIFIED:20250910T002342Z
UID:11438-1757685600-1757692800@www.ibs.re.kr
SUMMARY:Decomposing causality into its synergistic\, unique\, and redundant components - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Decomposing causality into its synergistic\, unique\, and redundant components” by Álvaro Martínez-Sánchez et al.\, Nature Communications\, 2024. \nAbstract \nCausality lies at the heart of scientific inquiry\, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role\, current methods for causal inference face significant challenges due to nonlinear dependencies\, stochastic interactions\, self-causation\, collider effects\, and influences from exogenous factors\, among others. While existing methods can effectively address some of these challenges\, no single approach has successfully integrated all these aspects. Here\, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant\, unique\, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations\, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods.
URL:https://www.ibs.re.kr/bimag/event/data-driven-model-discovery-and-model-selection-for-noisy-biological-systems-olive-cawiding/
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:20250905T140000
DTEND;TZID=Asia/Seoul:20250905T160000
DTSTAMP:20250901T020216Z
CREATED:20250825T080853Z
LAST-MODIFIED:20250901T020216Z
UID:11433-1757080800-1757088000@www.ibs.re.kr
SUMMARY:Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics” by J. Thompson et al.\, bioarxiv\, 2025. \nAbstract \nMicrobial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding\, controlling\, and harnessing their properties. Competition and cross-feeding of metabolites drives microbiome dynamics and functions. Existing mechanistic models that capture metabolite-mediated interactions in microbial communities have limited flexibility due to rigid assumptions. While machine learning models provide flexibility\, they require large datasets\, are challenging to interpret\, and can over-fit to experimental noise. To overcome these limitations\, we develop a physics-constrained machine learning model\, which we call the Neural Species Mediator (NSM)\, that combines a mechanistic model of metabolite dynamics with a machine learning component. The NSM is more accurate than mechanistic or machine learning components on experimental datasets and provides insights into direct biological interactions. In summary\, embedding a neural network into a mechanistic model of microbial community dynamics improves prediction performance and interpretability compared to its constituent mechanistic or machine learning components.
URL:https://www.ibs.re.kr/bimag/event/physics-constrained-neural-ordinary-differential-equation-models-to-discover-and-predict-microbial-community-dynamics-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:20250829T140000
DTEND;TZID=Asia/Seoul:20250829T160000
DTSTAMP:20250727T024418Z
CREATED:20250727T024418Z
LAST-MODIFIED:20250727T024418Z
UID:11348-1756476000-1756483200@www.ibs.re.kr
SUMMARY:Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains - Jinwoo Hyun
DESCRIPTION:In this talk\, we discuss the paper “Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains” by K. Lee. \nAbstract \nThe ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases\, considering the inherent properties of datasets. In tabular domains\, it is critical to effectively handle heterogeneous features (both categorical and numerical) in a unified manner and to grasp irregular functions like piecewise constant functions. To address the challenges in the self-supervised learning framework\, we propose a novel pretext task based on the classical binning method. The idea is straightforward: reconstructing the bin indices (either orders or classes) rather than the original values. This pretext task provides the encoder with an inductive bias to capture the irregular dependencies\, mapping from continuous inputs to discretized bins\, and mitigates the feature heterogeneity by setting all features to have category-type targets. Our empirical investigations ascertain several advantages of binning: compatibility with encoder architecture and additional modifications\, standardizing all features into equal sets\, grouping similar values within a feature\, and providing ordering information. Comprehensive evaluations across diverse tabular datasets corroborate that our method consistently improves tabular representation learning performance for a wide range of downstream tasks. The codes are available in the supplementary material.
URL:https://www.ibs.re.kr/bimag/event/binning-as-a-pretext-task-improving-self-supervised-learning-in-tabular-domains-jinwoo-hyun/
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:20250822T153000
DTEND;TZID=Asia/Seoul:20250822T173000
DTSTAMP:20250819T002937Z
CREATED:20250803T065046Z
LAST-MODIFIED:20250819T002937Z
UID:11366-1755876600-1755883800@www.ibs.re.kr
SUMMARY:Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters” by L. Xia et.al. Nature Communications\, 2024. \nAbstract \nTwo-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however\, it is well known that t-SNE and UMAP’s 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge\, we present a statistical method\, scDEED\, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell’s 2D-embedding neighbors and pre-embedding neighbors\, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover\, by minimizing the number of dubious cell embeddings\, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.
URL:https://www.ibs.re.kr/bimag/event/context-aware-deconvolution-of-cell-cell-communication-with-tensor-cell2cell-kevin-spinicci/
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:20250808T140000
DTEND;TZID=Asia/Seoul:20250808T160000
DTSTAMP:20250727T024732Z
CREATED:20250727T024732Z
LAST-MODIFIED:20250727T024732Z
UID:11351-1754661600-1754668800@www.ibs.re.kr
SUMMARY:Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan” by J. Shim et.al.\, npj digital medicine\, 2024. \nAbstract \nRecognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables\, we introduce CosinorAge\, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80\,000 midlife and older adults in the UK and US. A one-year increase in\nCosinorAge corresponded to 8–12% higher all-cause and cause-specific mortality risks and 3–14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning\, evidenced by an 8–33% reduction in self-rated health\nand a 3–23% decline in health-related quality of life score\, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover\, we\nillustrated a heterogeneous impact of circadian parameters associated with biological aging\, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers\, diminishing to a 7-minute difference in older adults\n(>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge’s potential as a scalable\, economic\, and digital solution for promoting healthy longevity\, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently\, our research contributes to advancing preventive measures in digital medicine.
URL:https://www.ibs.re.kr/bimag/event/circadian-rhythm-analysis-using-wearable-based-accelerometry-as-a-digital-biomarker-of-aging-and-healthspan-yun-min-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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250801T140000
DTEND;TZID=Asia/Seoul:20250801T160000
DTSTAMP:20250727T024047Z
CREATED:20250727T024030Z
LAST-MODIFIED:20250727T024047Z
UID:11346-1754056800-1754064000@www.ibs.re.kr
SUMMARY:Quantifying the energy landscape of high-dimensional oscillatory systems by diffusion decomposition - Eui Min Jeong
DESCRIPTION:In this talk\, we discuss the paper “Quantifying the energy landscape of high-dimensional oscillatory systems by diffusion decomposition” by S. Bian et.al.\, Cell Reports Physical Science\, 2025. \nAbstract \nHigh-dimensional networks producing oscillatory dynamics are ubiquitous in biological systems. Unraveling the mechanism of oscillatory dynamics in biological networks with stochastic perturbations becomes of paramount significance. Although the classical energy landscape theory provides a tool to study this problem in multistable systems and explain cellular functions\, it remains challenging to accurately quantify the landscape for high-dimensional oscillatory systems. Here\, we propose an approach called the diffusion decomposition of Gaussian approximation (DDGA). We demonstrate the efficacy of the DDGA in quantifying the energy landscape of oscillatory systems and corresponding stochastic dynamics in comparison with existing approaches. By further applying the DDGA to high-dimensional biological networks\, we are able to uncover more intricate biological mechanisms efficiently\, which deepens our understanding of cellular functions.
URL:https://www.ibs.re.kr/bimag/event/quantifying-the-energy-landscape-of-high-dimensional-oscillatory-systems-by-diffusion-decomposition-eui-min-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
END:VCALENDAR