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METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
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:20250404T110000
DTEND;TZID=Asia/Seoul:20250404T120000
DTSTAMP:20260521T205200
CREATED:20250217T080308Z
LAST-MODIFIED:20250217T080308Z
UID:10771-1743764400-1743768000@www.ibs.re.kr
SUMMARY:A lognormal Poisson model for single cell transcriptomic normalization - Fred Wright
DESCRIPTION:Abstract \nThe advent of single-cell transcriptomics has brought a greatly improved understanding of the heterogeneity of gene expression across cell types\, with important applications in developmental biology and cancer research. Single-cell RNA sequencing datasets\, which are based on tags called universal molecular identifiers\, typically include a large number of zeroes. For such datasets\, genes with even moderate expression may be poorly represented in sequencing count matrices. Standard pipelines often retain only a small subset of genes for further analysis\, but we address the problem of estimating relative expression across the entire transcriptome by adopting a multivariate lognormal Poisson count model. We propose empirical Bayes estimation procedures to estimate latent cell-cell correlations\, and to recover meaningful estimates for genes with low expression. For small groups of cells\, an important sampling procedure uses the full cell-cell correlation structure and is computationally feasible. For larger datasets\, we propose a gene-level shrinkage procedure that has favorable performance for datasets with approximately compound symmetric cell-cell correlation. A fast procedure that incorporates matrix approximations is also promising\, and extensible to very large datasets. We apply our approaches to simulated and real datasets\, and demonstrate favorable performance in comparisons to competing normalization approaches. We further illustrate the applications of our approach in downstream analyses\, including cell-type clustering and identification. \n 
URL:https://www.ibs.re.kr/bimag/event/a-lognormal-poisson-model-for-single-cell-transcriptomic-normalization-fred-wright/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/02/Fred_wright-e1739779380180.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250404T140000
DTEND;TZID=Asia/Seoul:20250404T160000
DTSTAMP:20260521T205200
CREATED:20250326T091007Z
LAST-MODIFIED:20250330T013324Z
UID:10919-1743775200-1743782400@www.ibs.re.kr
SUMMARY:Accurate predictions on small data with a tabular foundation model - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Accurate predictions on small data with a tabular foundation model” by Noah Hollmann et al.\, Nature (2025). \nAbstract \nTabular 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 missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models\, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories3\,4\,5\, gradient-boosted decision trees6\,7\,8\,9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN)\, a tabular foundation model that outperforms all previous methods on datasets with up to 10\,000 samples by a wide margin\, using substantially less training time. In 2.8 s\, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model\, this model also allows fine-tuning\, data generation\, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets\, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields\, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains.
URL:https://www.ibs.re.kr/bimag/event/a-differentiable-gillespie-algorithm-for-simulating-chemical-kinetics-parameter-estimation-and-designing-synthetic-biological-circuits-dongju-lim/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250411T140000
DTEND;TZID=Asia/Seoul:20250411T160000
DTSTAMP:20260521T205201
CREATED:20250327T010416Z
LAST-MODIFIED:20250327T010416Z
UID:10921-1744380000-1744387200@www.ibs.re.kr
SUMMARY:Entrainment and multi-stability of the p53 oscillator in human cells - Eui Min Jeong
DESCRIPTION: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. \nAbstract  \nThe 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 the theory of synchronization and entrainment\, we developed a mathematical model and experimental system to test the ability of the p53 oscillator to entrain to external drug pulses of various periods and strengths. We found that the p53 oscillator can be locked and entrained to a wide range of entrainment modes. External periods far from p53’s natural oscillations increased the heterogeneity between individual cells whereas stronger inputs reduced it. Single-cell measurements allowed deriving the phase response curves (PRCs) and multiple Arnold tongues of p53. In addition\, multi-stability and non-linear behaviors were mathematically predicted and experimentally detected\, including mode hopping\, period doubling\, and chaos. Our work revealed critical dynamical properties of the p53 oscillator and provided insights into understanding and controlling it. A record of this paper’s transparent peer review process is included in the supplemental information.
URL:https://www.ibs.re.kr/bimag/event/entrainment-and-multi-stability-of-the-p53-oscillator-in-human-cells-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:20250418T140000
DTEND;TZID=Asia/Seoul:20250418T160000
DTSTAMP:20260521T205201
CREATED:20250327T010619Z
LAST-MODIFIED:20250327T010619Z
UID:10923-1744984800-1744992000@www.ibs.re.kr
SUMMARY:Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series - Yun Min Song
DESCRIPTION: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. \nAbstract  \nTransfer entropy (TE) is a widely used tool for quantifying causal relationships in stochastic dynamical systems. Traditionally\, TE and its conditional variants are applied pairwise between dynamic variables to infer these relationships. However\, identifying key drivers in such systems requires a measure of the causal influence exerted by each component on the entire system. I propose using outgoing transfer entropy (OutTE)\, the transfer entropy from a given variable to the collection of remaining variables\, to quantify the causal influence of the variable on the rest of the system. Conversely\, the incoming transfer entropy (InTE) is also defined to quantify the causal influence received by a component from the rest of the system. Since OutTE and InTE involve transfer entropy between univariate and multivariate time series\, naive estimation methods can result in significant errors\, especially when the number of variables is large relative to the number of samples. To address this\, I introduce a novel estimation scheme that computes outgoing and incoming TE only between significantly interacting partners. The feasibility and effectiveness of this approach are demonstrated using synthetic data and real oral microbiota data. The method successfully identifies the bacterial species known to be key players in the bacterial community\, highlighting its potential for uncovering causal drivers in complex systems.
URL:https://www.ibs.re.kr/bimag/event/identifying-key-drivers-in-a-stochastic-dynamical-system-through-estimation-of-transfer-entropy-between-univariate-and-multivariate-time-series-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:20250422T160000
DTEND;TZID=Asia/Seoul:20250422T170000
DTSTAMP:20260521T205201
CREATED:20250421T005522Z
LAST-MODIFIED:20250422T064931Z
UID:10994-1745337600-1745341200@www.ibs.re.kr
SUMMARY:Dimensionality Reduction and Summary-Statistical Modeling in Genetic Studies - Fatemeh Yavartanoo
DESCRIPTION:Abstract: \nThis presentation introduces DRLPC and a refined summary-statistics method to improve genetic association analysis. Applications to cognition\, neurodegenerative diseases\, and high cholesterol are discussed\, with future directions in single-cell analysis and drug target discovery.
URL:https://www.ibs.re.kr/bimag/event/tba-fatemeh-yavartanoo/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/04/1705897753193.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250428T110000
DTEND;TZID=Asia/Seoul:20250428T120000
DTSTAMP:20260521T205201
CREATED:20250414T004912Z
LAST-MODIFIED:20250420T085852Z
UID:10975-1745838000-1745841600@www.ibs.re.kr
SUMMARY:FoodSeq: Using Genomics to Track and Study Diet - Lawrence David
DESCRIPTION:Abstract\nDietary assessment is crucial for understanding the relationship between diet and health. Yet traditional recall-based methods for tracking diet often face challenges like participant compliance and accurate recall. To address these issues\, our lab at Duke University has developed FoodSeq\, a genomic approach to track food intake through DNA sequencing of stool samples. In this talk\, I will explain how FoodSeq can identify and quantify dietary species\, allowing for objective and comprehensive monitoring of food consumption. We will explore the methodology behind FoodSeq\, including DNA extraction\, amplification\, and sequencing\, as well as data analysis. I will then present case studies demonstrating how FoodSeq can be used in clinical studies involving patients undergoing hematopoietic stem cell transplant\, highlighting the potential to contribute insights into nutrition\, health\, and the microbiome.
URL:https://www.ibs.re.kr/bimag/event/foodseq-using-genomics-to-track-and-study-diet-lawrence-david/
LOCATION:Conference room\, (B109)\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/04/0604222-e1745139516483.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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