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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250801T140000
DTEND;TZID=Asia/Seoul:20250801T160000
DTSTAMP:20260423T030304
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250808T140000
DTEND;TZID=Asia/Seoul:20250808T160000
DTSTAMP:20260423T030304
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:20250822T153000
DTEND;TZID=Asia/Seoul:20250822T173000
DTSTAMP:20260423T030304
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:20250829T140000
DTEND;TZID=Asia/Seoul:20250829T160000
DTSTAMP:20260423T030304
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
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