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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20251107T100000
DTEND;TZID=Asia/Seoul:20251107T120000
DTSTAMP:20260422T175017
CREATED:20251026T141100Z
LAST-MODIFIED:20251107T000601Z
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SUMMARY:Principled PCA separates signal from noise in omics count data - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “Principled PCA separates signal from noise in omics count data” by Jay S. Stanley III et al.\, bioarxiv\, 2025.  \nAbstract \nPrincipal component analysis (PCA) is indispensable for processing high-throughput omics datasets\, as it can extract meaningful biological variability while minimizing the influence of noise. However\, the suitability of PCA is contingent on appropriate normalization and transformation of count data\, and accurate selection of the number of principal components; improper choices can result in the loss of biological information or corruption of the signal due to excessive noise. Typical approaches to these challenges rely on heuristics that lack theoretical foundations. In this work\, we present Biwhitened PCA (BiPCA)\, a theoretically grounded framework for rank estimation and data denoising across a wide range of omics modalities. BiPCA overcomes a fundamental difficulty with handling count noise in omics data by adaptively rescaling the rows and columns – a rigorous procedure that standardizes the noise variances across both dimensions. Through simulations and analysis of over 100 datasets spanning seven omics modalities\, we demonstrate that BiPCA reliably recovers the data rank and enhances the biological interpretability of count data. In particular\, BiPCA enhances marker gene expression\, preserves cell neighborhoods\, and mitigates batch effects. Our results establish BiPCA as a robust and versatile framework for high-throughput count data analysis.
URL:https://www.ibs.re.kr/bimag/event/from-noise-to-models-to-numbers-evaluating-negative-binomial-models-and-parameter-estimations-in-single-cell-rna-seq-hyun-kim/
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251121T100000
DTEND;TZID=Asia/Seoul:20251121T120000
DTSTAMP:20260422T175017
CREATED:20251026T141157Z
LAST-MODIFIED:20251121T000249Z
UID:11794-1763719200-1763726400@www.ibs.re.kr
SUMMARY:Modeling personalized heart rate response to exercise and environmental factors with wearables data - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Modeling personalized heart rate response to exercise and environmental factors with wearables data” by Nazaret et al.\, npj digital medicine\, 2023. \nAbstract \nHeart rate (HR) response to workout intensity re ects tness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory tness. However\, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with exible neural network components to learn a personalized\, multidimensional representation of tness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors\, from area topography to weather and instantaneous workout\nintensity. Our approach ef ciently ts the hybrid model to a large set of 270\,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces tness representations that accurately predict full HR response to exercise intensity in future workouts\, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory tness\, such as VO2 max (explained variance\n0.81 ± 0.003). Lastly\, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate\, e.g.\, high temperatures can increase heart rate by 10%. Combining physiological ODEs with exible neural networks can yield interpretable\, robust\, and expressive models for health applications.
URL:https://www.ibs.re.kr/bimag/event/journal-club-dongju-lim/
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
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