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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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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250404T110000
DTEND;TZID=Asia/Seoul:20250404T120000
DTSTAMP:20260521T220343
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250404T140000
DTEND;TZID=Asia/Seoul:20250404T160000
DTSTAMP:20260521T220343
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
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