기초과학연구원 의생명수학그룹
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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. Abstract High-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 … |
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Abstract Topological Data Analysis (TDA) has emerged as a powerful framework for uncovering meaningful structure in high-dimensional, complex datasets. In this talk, we present two applications of TDA in analyzing patterns, one in the tumor microenvironment (TME) and the other in high-resolution chemical profiling. In the first case, we develop a TDA-based framework to quantify malignant-immune cell interactions … |
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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. Abstract Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging … |
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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. Abstract Two-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 … |
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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 … |
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