Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality – Yun Min Song

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality" by H. Yuan et.al, npj digital medicine, 2024, at the Journal Club. Abstract  Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for

A cell atlas foundation model for scalable search of similar human cells – Kevin Spinicci

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "A cell atlas foundation model for scalable search of similar human cells" by Graham Heimberg et.al., Nature, 2024 at the Journal Club. Abstract Single-cell RNA sequencing has profiled hundreds of millions of human cells across organs, diseases, development and perturbations to date. Mining these growing atlases could reveal

Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries – Brenda Gavina

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity" by Irena Balzekas et.al., Plos Com., 2024. Abstract Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses

Constraining nonlinear time series modeling with the metabolic theory of ecology – Olive Cawiding

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Constraining nonlinear time series modeling with the metabolic theory of ecology" by S.B. Munch et.al., PNAS, 2023. Abstract Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but

Quantifying information accumulation encoded in the dynamics of biochemical signaling – Kang Min Lee

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Quantifying information accumulation encoded in the dynamics of biochemical signaling" by Y. Tang, et.al, Nature Communications, 2021. Abstract Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved

The Large Language Models on Biomedical Data Analysis: A Survey – Myna Lim

In this talk, we discuss the paper "The Large Language Models on Biomedical Data Analysis: A Survey" by Wei Lan et.al, IEEE J. Biomedical and Health Informatics, 2025, at the Journal Club. Abstract  With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However,

A biological model of nonlinear dimensionality reduction – Shingo Gibo

In this talk, we discuss the paper "A biological model of nonlinear dimensionality reduction" by K. Yoshida and T. Toyoizumi, Science Advances, 2025, at the Journal Club. Abstract Obtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor

Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach – Gyuyoung Hwang

In this talk, we discuss the paper "Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach" by R.C. Vendrell et.al., Sci. Adv. 2024 at the Journal Club. Abstract De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no

Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain – Hyun Kim

In this talk, we discuss the paper "Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain" by Rubén Calvo et al., Physical Review Letters 2024, at the Journal Club. Abstract Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly

Accurate predictions on small data with a tabular foundation model – Dongju Lim

In this talk, we discuss the paper "Accurate predictions on small data with a tabular foundation model" by Noah Hollmann et al., Nature (2025). Abstract Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in

Entrainment and multi-stability of the p53 oscillator in human cells – Eui Min Jeong

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper, "Entrainment and multi-stability of the p53 oscillator in human cells" by Alba Jiménez et al., Cell Systems, 2024. Abstract  The tumor suppressor p53 responds to cellular stress and activates transcription programs critical for regulating cell fate. DNA damage triggers oscillations in p53 levels with a robust period. Guided by

Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series – Yun Min Song

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series" by Julian Lee, Physical Review E, 2025. Abstract  Transfer entropy (TE) is a widely used tool for quantifying causal relationships in stochastic dynamical systems. Traditionally, TE and its conditional

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