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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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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250404T140000
DTEND;TZID=Asia/Seoul:20250404T160000
DTSTAMP:20260423T080714
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250411T140000
DTEND;TZID=Asia/Seoul:20250411T160000
DTSTAMP:20260423T080714
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:20260423T080714
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
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