Heejung Shim - Modelling spatial transcriptomics: from flexible cell-type deconvolution to multi-scale spatial factor analysis
2026-05-18 12:30 - 13:30 (Asia/Seoul) | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract:
Spatial transcriptomics enables the study of gene expression within its spatial context, but introduces key statistical challenges, including mixed cellular composition and complex spatial structure. In this talk, I present two complementary modelling approaches.First, I introduce FlexiDeconv, a cell-type deconvolution method based on a modified Latent Dirichlet Allocation framework. A key feature of this method is its flexible use of reference information, allowing the model to balance prior information from scRNA-seq with signals from observed spatial data, and to adapt when the reference is incomplete or mismatched, a common challenge in practice.I then present WaviFM, a wavelet-based Bayesian sparse factor model that captures spatial gene expression patterns across multiple spatial scales, enabling the detection of both fine and broad spatial patterns. In addition, WaviFM can incorporate gene-set information to guide factor inference, while allowing for uncertainty and potential errors in these annotations.Together, these methods illustrate how flexible modelling of prior information and multi-scale modelling of spatial structure can improve our ability to extract biologically meaningful signals from spatial transcriptomics data.

Mathematics of diffusive signaling - Alan Lindsay
2026-05-22 11:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Diffusive transport is one of the most fundamental mechanisms by which information, mass, and chemical signals propagate in physical and biological systems. In many settings—ranging from cellular signaling to chemical sensing—communication is mediated by particles undergoing random motion and interacting with small, spatially localized targets. This talk explores the mathematical structures underlying diffusive signaling, emphasizing how geometry, stochasticity, and multiscale effects shape signal detection and reliability. Using tools from stochastic processes, partial differential equations, and asymptotic analysis, I will describe how seemingly microscopic features can exert a dominant influence on macroscopic signaling outcomes, and highlight recent progress on quantifying signal strength, timing, and variability in complex geometries.

Zoom : 997 8258 4700 (pw : [REDACTED]

Causal Generalist Medical AI - Hongtu Zhu
2026-05-27 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
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.

Zoom : 997 8258 4700 (pw : [REDACTED]

Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies - Chitaranjan Mahapatra
2026-05-29 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
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.
Abstract:
Accelerometers 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.

Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations - Gyuyoung Hwang
2026-06-05 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations” by Zheng-Meng Zhai et al., Nature Communications, 2025.
Abstract:
In applications, an anticipated issue is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed? We address this challenge by developing a hybrid transformer and reservoir-computing scheme. The transformer is trained without using data from the target system, but with essentially unlimited synthetic data from known chaotic systems. The trained transformer is then tested with the sparse data from the target system, and its output is further fed into a reservoir computer for predicting its long-term dynamics or the attractor. The proposed hybrid machine-learning framework is tested using various prototypical nonlinear systems, demonstrating that the dynamics can be faithfully reconstructed from reasonably sparse data. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the situation where training data do not exist and the observations are random and sparse.

Insulin resistance prediction from wearables and routine blood biomarkers - Dongju Lim
2026-06-12 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Insulin resistance prediction from wearables and routine blood biomarkers” by Ahmed A. Metwally et al., Nature, 2026.

Abstract:
Insulin 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.
