기초과학연구원 의생명수학그룹
In this talk, we discuss the paper "Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains" by K. Lee. Abstract The 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 …
Abstract The creation and inference of mathematical models is central to modern scientific discovery in the life sciences. As more realism is demanded of models, however, the conventional framework of biology-guided model proposal, discretization, parameter estimation, and model refinement becomes unwieldy, expensive, and computationally daunting. Recent advances in Weak form-based Scientific Machine Learning (WSciML) allow …
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. Abstract Microbial 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 …
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. Abstract Causality 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 …
In this talk, we discuss the paper "SCassist: An AI Based Workflow Assistant for Single-Cell Analysis " by Vijayaraj Nagarajan et al., bioarxiv, 2025. Abstract Single-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 …
Abstract Sleep is commonly analysed as an independent factor. However, because of the 24-hour constraints on a day, changes in sleep will co-occur with changes in remaining time use. This talk introduces compositional data analysis (CoDA) for sleep research. CoDA is illustrated using 24-hour sleep and activity data from accelerometry, first cross-sectionally showing associations between …
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. Abstract Smartwatches 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 …
Abstract Background : Excess mortality captures both the direct and indirect impacts of the pandemic. We examine (1) within-country heterogeneity by healthcare access over distinct viral waves in Korea, and (2) cross-country associations between excess mortality and preparedness (Global Health Security, GHS), stratified by IMF development stage. Methods : Study 1 assembled a region-level panel …
Abstract Many processes in the life sciences are inherently multi-scale and dynamic. Spatial structures and patterns vary across levels of organisation, from molecular to multi-cellular to multi-organism. With more sophisticated mechanistic models and data available, quantitative tools are needed to study their evolution in space and time. Topological data analysis (TDA) provides a multi-scale summary …
Abstract Smartphones and wearable devices provide a major opportunity to transform our understanding of the mechanisms, determinants, and consequences of diseases. For example, around 9 in 10 people own a smartphone in the United Kingdom, while one-fifth of US adults own wearable technologies. This high level of device ownership means that many people could contribute …
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). Abstract We propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution …
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. Abstract Although 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 …