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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
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250806T150000
DTEND;TZID=Asia/Seoul:20250806T170000
DTSTAMP:20260422T131035
CREATED:20250804T010321Z
LAST-MODIFIED:20250824T041110Z
UID:11368-1754492400-1754499600@www.ibs.re.kr
SUMMARY:Jooyoung Hahn - Topological Data Analysis with two applications: Tumor Microenvironment and  2D Chromatography with High-Resolution Mass Spectrometry
DESCRIPTION:Abstract  \nTopological Data Analysis (TDA) has emerged as a powerful framework for uncovering meaningful structure in high-dimensional\, complex datasets. In this talk\, we present two applications of TDA in analyzing patterns\, one in the tumor microenvironment (TME) and the other in high-resolution chemical profiling. In the first case\, we develop a TDA-based framework to quantify malignant-immune cell interactions in Diffuse Large B Cell Lymphoma using multiplex immunofluorescence imaging. By introducing Topological Malignant Clusters (TopMC) and leveraging persistence diagrams\, we capture both global infiltration patterns and local density-based features. This robust approach enables consistent prognostic assessment regardless of tumor region heterogeneity and reveals correlations with patient survival. In the second application\, we utilize the Ball Mapper algorithm to simplify and visualize high-dimensional data obtained from 2D Chromatography with high-resolution mass spectrometry. This enables interpretable chemical profiling of complex mixtures and supports tasks such as sample authentication and environmental analysis. Together\, these studies demonstrate the versatility and interpretability of TDA for extracting biologically and chemically meaningful information. \nSeminar Video Link: https://www.youtube.com/watch?v=mz9pY6nk3n4&t=12s
URL:https://www.ibs.re.kr/bimag/event/jooyoung-hahn-topological-data-analysis-with-two-applications-tumor-microenvironment-and-2d-chromatography-with-high-resolution-mass-spectrometry/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250801T140000
DTEND;TZID=Asia/Seoul:20250801T160000
DTSTAMP:20260422T131035
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:20250725T140000
DTEND;TZID=Asia/Seoul:20250725T160000
DTSTAMP:20260422T131035
CREATED:20250628T123019Z
LAST-MODIFIED:20250721T002532Z
UID:11218-1753452000-1753459200@www.ibs.re.kr
SUMMARY:Effective Markovian dynamics method of solving non-Markovian dynamics of stochastic gene expression - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Effective Markovian dynamics method of solving non-Markovian dynamics of stochastic gene expression” by Youming Li and Chen Jia\, Physical Review Letters\, to appear. \nAbstract \nExperiments have shown that over 10% of proteins are degraded non-exponentially. Gene expression models for non-exponentially degraded proteins are notoriously difficult to solve since the underlying stochastic dynamics is non-Markovian. Here we develop an effective Markovian dynamics (EMD) method which converts a large class of non-Markovian models into effective Markovian ones so that they have the same mRNA and protein distributions at any fixed time. Using the EMD approach\, we analytically solve some classical gene expression models with non-exponential or delayed protein decay\, whose exact distributions are previously unknown and fail to be obtained using conventional queueing theory. Our theory successfully explains why non-exponentially degraded proteins on average have smaller mRNA-protein correlation than exponentially degraded proteins\, and it predicts that bimodality is significantly enhanced in the presence of delayed protein degradation.
URL:https://www.ibs.re.kr/bimag/event/action-functional-as-an-early-warning-indicator-in-the-space-of-probability-measures-via-schrodinger-bridge-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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250721T110000
DTEND;TZID=Asia/Seoul:20250721T120000
DTSTAMP:20260422T131035
CREATED:20250617T084231Z
LAST-MODIFIED:20250617T084231Z
UID:11189-1753095600-1753099200@www.ibs.re.kr
SUMMARY:Jae-Kwang Kim - Weight calibration for causal inference and transfer learning
DESCRIPTION:Abstract: Weight calibration is a popular technique in handling covariate-shift problem in causal inference. It can be viewed as a dual optimization problem for incorporating the implicit regression model. We introduce the generalized entropy calibration as a general tool for weight calibration. Several interesting applications will be introduced in the context of causal inference. Furthermore\, weight calibration can be used to transfer learning\, which combines information from two different samples\, one for source data and the other for target data.
URL:https://www.ibs.re.kr/bimag/event/jae-kwang-kim-weight-calibration-for-causal-inference-and-transfer-learning/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250718T140000
DTEND;TZID=Asia/Seoul:20250718T160000
DTSTAMP:20260422T131035
CREATED:20250701T022224Z
LAST-MODIFIED:20250701T022224Z
UID:11231-1752847200-1752854400@www.ibs.re.kr
SUMMARY:scGPT: toward building a foundation model for single-cell multi-omics using generative AI - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “scGPT: toward building a foundation model for single-cell multi-omics using generative AI” by Haotian Cui\, et.al. Nature Methods\, 2024. \nAbstract \nGenerative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically\, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly\, cells are defined by genes)\, our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data\, we have constructed a foundation model for single-cell biology\, scGPT\, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning\, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation\, multi-batch integration\, multi-omic integration\, perturbation response prediction and gene network inference.
URL:https://www.ibs.re.kr/bimag/event/scgpt-toward-building-a-foundation-model-for-single-cell-multi-omics-using-generative-ai-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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250711T140000
DTEND;TZID=Asia/Seoul:20250711T160000
DTSTAMP:20260422T131035
CREATED:20250628T122808Z
LAST-MODIFIED:20250628T122808Z
UID:11216-1752242400-1752249600@www.ibs.re.kr
SUMMARY:Optimal transport for generating transition states in chemical reactions - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Optimal transport for generating transition states in chemical reactions” by C. Duan et.al.\, Nat. Machine. Intelligence\, 2025. \nAbstract \nTransition states (TSs) are transient structures that are key to understanding reaction mechanisms and designing catalysts but challenging to capture in experiments. Many optimization algorithms have been developed to search for TSs computationally. Yet\, the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high\, posing challenges for their applications in building large reaction networks for reaction exploration. Here we developed React-OT\, an optimal transport approach for generating unique TS structures from reactants and products. React-OT generates highly accurate TS structures with a median structural root mean square deviation of 0.053 Å and median barrier height error of 1.06 kcal mol−1 requiring only 0.4 s per reaction. The root mean square deviation and barrier height error are further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory\, GFN2-xTB. We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms.
URL:https://www.ibs.re.kr/bimag/event/optimal-transport-for-generating-transition-states-in-chemical-reactions-gyuyoung-hwang/
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:20250704T140000
DTEND;TZID=Asia/Seoul:20250704T160000
DTSTAMP:20260422T131035
CREATED:20250526T004910Z
LAST-MODIFIED:20250609T001902Z
UID:11146-1751637600-1751644800@www.ibs.re.kr
SUMMARY:Machine learning methods trained on simple models can predict critical transitions in complex natural systems - Shingo Gibo
DESCRIPTION:In this talk\, we discuss the paper “Machine learning methods trained on simple models can predict critical transitions in complex natural systems” by  Smita Deb\, Sahil Sidheekh\, Christopher F. Clements\, Narayanan C. Krishnan\, and Partha S. Dutta\, in Royal Society Open Science\, (2022). \nAbstract:  \nForecasting sudden changes in complex systems is a critical but challenging task\, with previously developed methods varying widely in their reliability. Here we develop a novel detection method\, using simple theoretical models to train a deep neural network to detect critical transitions—the Early Warning Signal Network (EWSNet). We then demonstrate that this network\, trained on simulated data\, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly\, our model appears to capture latent properties in time series missed by previous warning signals approaches\, allowing us to not only detect if a transition is approaching\, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems\, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.
URL:https://www.ibs.re.kr/bimag/event/machine-learning-methods-trained-on-simple-models-can-predict-critical-transitions-in-complex-natural-systems-shingo-gibo/
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:20250703T160000
DTEND;TZID=Asia/Seoul:20250703T170000
DTSTAMP:20260422T131035
CREATED:20250628T074404Z
LAST-MODIFIED:20250630T055202Z
UID:11207-1751558400-1751562000@www.ibs.re.kr
SUMMARY:Jihun Han - Bridging PDEs and machine learning
DESCRIPTION:Abstract: This talk consists of two main parts. In the first part\, I will discuss a numerical method for solving PDEs based on a stochastic representation of the solution. This approach captures the underlying particle dynamics associated with the physical processes described by the PDE. By aggregating information from the particles’ collective exploration\, the method iteratively reinforces the approximation toward the solution. I will cover its analysis regarding the trainability and highlight its effectiveness across a broad class of problems\, including elliptic equations with interfaces\, multiscale structures\, and perforated domains\, as well as hyperbolic-type problems such as the Eikonal and Burgers equations.\nIn the second part\, I will present a method for learning in-between imagery dynamics. This approach integrates PDE models within latent spaces to enhance both learning capability and interpretability. Notably\, this method demonstrates robustness in capturing intricate dynamics\, such as rotation and outflow\, which pose significant challenges for current state-of-the-art optimal transport methods.
URL:https://www.ibs.re.kr/bimag/event/ji-hoon-han-bridging-pdes-and-machine-learning/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250627T160000
DTEND;TZID=Asia/Seoul:20250627T170000
DTSTAMP:20260422T131035
CREATED:20250615T110211Z
LAST-MODIFIED:20250615T110211Z
UID:11180-1751040000-1751043600@www.ibs.re.kr
SUMMARY:U Jin Choi - Simulation-Free Schrodinger Bridges Via Score and Flow Matching (by Tong et al\, AISTATS 2024).
DESCRIPTION:Abstract: 임의로 정한 Initial Distribution Q1 와 Terminal Distribution Q2가 주어 졌을 때 시점과 종점 사이의 contiinious time상에  정의 되는 의미 있는 최적의 Probability Path Measure P 를 찾는 Schrodinger Bridges Problem 은 자연과학\,공학\, 의료 및 생명공학\,경제학 및 금융공학 등의 여러 분야에 나타나는 모델들을 푸는 Unified AI Model 사용 되고 있습니다. Schrodinger Bridges Problem은  유일한 해가 존재 하는 정리는( Follmer\,1988)  증명 되었으므로 데이터를 이용하여  Neural Network Models에 대한 효율적으로 학습방법\,  빠른 알고리즘 연구에 집중 되고 있습니다. Tong et al 연구팀은 2023년 부터 ODE에 기반한 획기적인 생성모델인  Flow Matching for Generative Modeling 기법을  SDE 기반 Diffusion Generative Models에 접목하여 Schrodinger Bridges Problem의 해법을 제시하였습니다.
URL:https://www.ibs.re.kr/bimag/event/u-jin-choi-simulation-free-schrodinger-bridges-via-score-and-flow-matching-by-tong-et-al-aistats-2024/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250627T140000
DTEND;TZID=Asia/Seoul:20250627T160000
DTSTAMP:20260422T131035
CREATED:20250426T143642Z
LAST-MODIFIED:20250609T001825Z
UID:11067-1751032800-1751040000@www.ibs.re.kr
SUMMARY:Data splitting to avoid information leakage with DataSAIL - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper\, “Data splitting to avoid information leakage with DataSAIL” by Roman Joeres\, et al.\, Nature Communications\, 2025. \nAbstract \nInformation leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model’s training\, it risks memorizing the training data instead of learning generalizable properties. This can lead to inflated performance metrics that do not reflect the actual performance at inference time. We present DataSAIL\, a versatile Python package to facilitate leakage-reduced data splitting to enable realistic evaluation of machine learning models for biological data that are intended to be applied in out-of-distribution scenarios. DataSAIL is based on formulating the problem to find leakage-reduced data splits as a combinatorial optimization problem. We prove that this problem is NP-hard and provide a scalable heuristic based on clustering and integer linear programming. Finally\, we empirically demonstrate DataSAIL’s impact on evaluating biomedical machine learning models.
URL:https://www.ibs.re.kr/bimag/event/data-splitting-to-avoid-information-leakage-with-datasail-myna-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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250620T110000
DTEND;TZID=Asia/Seoul:20250620T123000
DTSTAMP:20260422T131035
CREATED:20250426T143500Z
LAST-MODIFIED:20250617T001232Z
UID:11064-1750417200-1750422600@www.ibs.re.kr
SUMMARY:Large language models for scientific discovery in molecular property prediction - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “Large language models for scientific discovery in molecular property prediction” by Yizhen Zheng et.al.\, nature machine intelligence\, 2025. \nAbstract \nLarge language models (LLMs) are a form of artificial intelligence system encapsulating vast knowledge in the form of natural language. These systems are adept at numerous complex tasks including creative writing\, storytelling\, translation\, question-answering\, summarization and computer code generation. Although LLMs have seen initial applications in natural sciences\, their potential for driving scientific discovery remains largely unexplored. In this work\, we introduce LLM4SD\, a framework designed to harness LLMs for driving scientific discovery in molecular property prediction by synthesizing knowledge from literature and inferring knowledge from scientific data. LLMs synthesize knowledge by extracting established information from scientific literature\, such as molecular weight being key to predicting solubility. For inference\, LLMs identify patterns in molecular data\, particularly in Simplified Molecular Input Line Entry System-encoded structures\, such as halogen-containing molecules being more likely to cross the blood–brain barrier. This information is presented as interpretable knowledge\, enabling the transformation of molecules into feature vectors. By using these features with interpretable models such as random forest\, LLM4SD can outperform the current state of the art across a range of benchmark tasks for predicting molecular properties. We foresee it providing interpretable and potentially new insights\, aiding scientific discovery in molecular property prediction.
URL:https://www.ibs.re.kr/bimag/event/large-language-models-for-scientific-discovery-in-molecular-property-prediction-aqsa-awan/
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:20250613T093000
DTEND;TZID=Asia/Seoul:20250613T110000
DTSTAMP:20260422T131035
CREATED:20250609T002038Z
LAST-MODIFIED:20250609T033628Z
UID:11164-1749807000-1749812400@www.ibs.re.kr
SUMMARY:Deep learning for universal linear embeddings of nonlinear dynamics - Hyukpyo Hong
DESCRIPTION:In this talk\, we discuss the paper “Deep learning for universal linear embeddings of nonlinear dynamics” by B. Lusch\, J. N. Kutz\, and S. Brunton\, Nat. Comm. 2018. \nAbstract  \nIdentifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction\, estimation\, and control using linear theory. The Koopman operator is a leading data-driven embedding\, and its eigenfunctions provide intrinsic coordinates that globally linearize the dynamics. However\, identifying and representing these eigenfunctions has proven challenging. This work leverages deep learning to discover representations of Koopman eigenfunctions from data. Our network is parsimonious and interpretable by construction\, embedding the dynamics on a low-dimensional manifold. We identify nonlinear coordinates on which the dynamics are globally linear using a modified auto-encoder. We also generalize Koopman representations to include a ubiquitous class of systems with continuous spectra. Our framework parametrizes the continuous frequency using an auxiliary network\, enabling a compact and efficient embedding\, while connecting our models to decades of asymptotics. Thus\, we benefit from the power of deep learning\, while retaining the physical interpretability of Koopman embeddings.
URL:https://www.ibs.re.kr/bimag/event/hyukpyo-hong/
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:20250530T140000
DTEND;TZID=Asia/Seoul:20250530T160000
DTSTAMP:20260422T131035
CREATED:20250426T143239Z
LAST-MODIFIED:20250528T035910Z
UID:11061-1748613600-1748620800@www.ibs.re.kr
SUMMARY:Direct Estimation of Parameters in ODE Models Using WENDy - Kangmin Lee
DESCRIPTION:In this talk\, we discuss the paper “Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics” by David M. Bortz\, Daniel A. Messenger\, and Vanja Dukic\, Bulletin of Mathematical Biology\, 2023. \nAbstract \nWe introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers\, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data\, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems\, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form\, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework\, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions\, created from a set of C∞ bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology\, neuroscience\, and biochemistry\, including logistic growth\, Lotka-Volterra\, FitzHugh-Nagumo\, Hindmarsh-Rose\, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy.
URL:https://www.ibs.re.kr/bimag/event/quantifying-and-correcting-bias-in-transcriptional-parameter-inference-from-single-cell-data-kangmin-lee/
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:20250530T110000
DTEND;TZID=Asia/Seoul:20250530T120000
DTSTAMP:20260422T131035
CREATED:20250217T081212Z
LAST-MODIFIED:20250217T082031Z
UID:10780-1748602800-1748606400@www.ibs.re.kr
SUMMARY:Koopman operator approach to complex rhythmic systems - Hiroya Nakao
DESCRIPTION:Abstract \nSpontaneous rhythmic oscillations are widely observed in real-world systems. Synchronized rhythmic oscillations often provide important functions for biological or engineered systems. One of the useful theoretical methods for analyzing rhythmic oscillations is the phase reduction theory for weakly perturbed limit-cycle oscillators\, which systematically gives a low-dimensional description of the oscillatory dynamics using only the asymptotic phase of the oscillator. Recent advances in Koopman operator theory provide a new viewpoint on phase reduction\, yielding an operator-theoretic definition of the classical notion of the asymptotic phase and\, moreover\, of the amplitudes\, which characterize distances from the limit cycle. This led to the generalization of classical phase reduction to phase-amplitude reduction\, which can characterize amplitude deviations of the oscillator from the unperturbed limit cycle in addition to the phase along the cycle in a systematic manner. In the talk\, these theories are briefly reviewed and then applied to several examples of synchronizing rhythmic systems\, including biological oscillators\, networked dynamical systems\, and rhythmic spatiotemporal patterns.
URL:https://www.ibs.re.kr/bimag/event/koopman-operator-approach-to-complex-rhythmic-systems-hiroya-nakao/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/02/nakao-hiroya.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250521T160000
DTEND;TZID=Asia/Seoul:20250521T170000
DTSTAMP:20260422T131035
CREATED:20250217T080703Z
LAST-MODIFIED:20250217T080703Z
UID:10775-1747843200-1747846800@www.ibs.re.kr
SUMMARY:Simplified descriptions of stochastic oscillators - Benjamin Lindner
DESCRIPTION:Abstract \nMany natural systems exhibit oscillations that show sizeable fluctuations in frequency and amplitude. This variability can arise from a wide variety of physical mechanisms. Phase descriptions that work for deterministic oscillators have a limited applicability for stochastic oscillators. In my talk I review attempts to generalize the phase concept to stochastic oscillations\, specifically\, the mean-return-time phase and the asymptotic phase.\nFor stochastic systems described by Fokker-Planck and Kolmogorov-backward equations\, I introduce a mapping of the system’s variables to a complex pointer (instead of a real-valued phase) that is based on the eigenfunction of the Kolmogorov equation. Under the new (complex-valued) description\, the statistics of the oscillator’s spontaneous activity\, of its response to external perturbations\, and of the coordinated activity of (weakly) coupled oscillators\, is brought into a universal and greatly simplified form. The theory is tested for three theoretical models of noisy oscillators arising from fundamentally different mechanisms: a damped harmonic oscillator with dynamical noise\, a fluctuation-perturbed limit-cycle system\, and an excitable system in which oscillations require noise to occur.
URL:https://www.ibs.re.kr/bimag/event/simplified-descriptions-of-stochastic-oscillators-benjamin-lindner/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/02/Benjamin-Lindner-e1739779616840.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250509T140000
DTEND;TZID=Asia/Seoul:20250509T160000
DTSTAMP:20260422T131036
CREATED:20250426T142850Z
LAST-MODIFIED:20250507T002814Z
UID:11058-1746799200-1746806400@www.ibs.re.kr
SUMMARY:Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics” by Amitava Banerjee\, Sarthak Chandra\, and Edward Ott\, PNAS\, 2023. \nAbstract \nNetwork link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications\, e.g.\, estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases\, the measured time series data may be subject to limitations\, including limited duration\, low sampling rate\, observational noise\, and partial nodal state measurement. However\, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here\, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality\, transfer entropy\, and\, a machine learning-based method. Furthermore\, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.
URL:https://www.ibs.re.kr/bimag/event/chaos-is-not-rare-in-natural-ecosystems-olive-cawiding/
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:20250502T140000
DTEND;TZID=Asia/Seoul:20250502T160000
DTSTAMP:20260422T131036
CREATED:20250330T073307Z
LAST-MODIFIED:20250424T070416Z
UID:10929-1746194400-1746201600@www.ibs.re.kr
SUMMARY:Boolean modelling as a logic-based dynamic approach in systems medicine - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “Boolean modelling as a logic-based dynamic approach in systems medicine” by Ahmed Abdelmonem Hemedan et al.\, Computational and Structural biotechnology journal (2022). \nAbstract  \nMolecular mechanisms of health and disease are often represented as systems biology diagrams\, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models\, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive\, one for present or active. Because of this approximation\, Boolean modelling is applicable to large diagrams\, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally\, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.
URL:https://www.ibs.re.kr/bimag/event/boolean-modelling-as-a-logic-based-dynamic-approach-in-systems-medicine-kevin-spinicci/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20260422T131036
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250422T160000
DTEND;TZID=Asia/Seoul:20250422T170000
DTSTAMP:20260422T131036
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:20250418T140000
DTEND;TZID=Asia/Seoul:20250418T160000
DTSTAMP:20260422T131036
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:20250411T140000
DTEND;TZID=Asia/Seoul:20250411T160000
DTSTAMP:20260422T131036
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:20250404T140000
DTEND;TZID=Asia/Seoul:20250404T160000
DTSTAMP:20260422T131036
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:20250404T110000
DTEND;TZID=Asia/Seoul:20250404T120000
DTSTAMP:20260422T131036
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:20250328T140000
DTEND;TZID=Asia/Seoul:20250328T160000
DTSTAMP:20260422T131036
CREATED:20250302T133447Z
LAST-MODIFIED:20250327T010923Z
UID:10853-1743170400-1743177600@www.ibs.re.kr
SUMMARY:Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain” by Rubén Calvo et al.\, Physical Review Letters 2024\, at the Journal Club. \nAbstract \nRecent analyses\, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain\, compellingly support the hypothesis that neural dynamics operate near the edge of instability. However\, these and related analyses often fail to capture the intricate temporal structure of brain activity\, as they primarily rely on time-integrated measurements across neurons. Here\, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Furthermore\, we introduce a method for projecting brain activity onto a basis of spatiotemporal patterns\, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset\, we observe significant differences in criticality signatures\, effective dimensionality\, and spatiotemporal activity patterns between healthy subjects and individuals with Parkinson’s disease\, highlighting its potential impact.
URL:https://www.ibs.re.kr/bimag/event/journal-club-hyun-kim/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250328T110000
DTEND;TZID=Asia/Seoul:20250328T120000
DTSTAMP:20260422T131036
CREATED:20250217T075911Z
LAST-MODIFIED:20250217T081432Z
UID:10766-1743159600-1743163200@www.ibs.re.kr
SUMMARY:Dynamics and Decision Making in Single Cells - Galit Lahav
DESCRIPTION:Abstract \nIndividual human cancer cells often show different responses to the same treatment. In this talk I will share the quantitative experimental approaches my lab has developed for studying the fate and behavior of human cells at the single-cell level. I will focus on the tumor suppressor protein p53\, a transcription factor controlling genomic integrity and cell survival. In the last several years we have established the dynamics of p53 (changes in its levels over time) as an important mechanism controlling gene expression and guiding cellular outcomes. I will present recent studies from the lab demonstrating how studying p53 dynamics in response to radiation and chemotherapy in single cells can guide the design and schedule of combinatorial therapy\, and how the p53 oscillator can be used to study the principles and function of entertainment in Biology. I will also present new findings suggesting that p53’s post-translational modification state is altered between its first and second pulses of expression\, and the effects these have on gene expression programs over time.
URL:https://www.ibs.re.kr/bimag/event/dynamics-and-decision-making-in-single-cells-galit-lahav/
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/Galit-Lahav-e1739779209180.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250321T143000
DTEND;TZID=Asia/Seoul:20250321T163000
DTSTAMP:20260422T131036
CREATED:20250226T070501Z
LAST-MODIFIED:20250314T140235Z
UID:10811-1742567400-1742574600@www.ibs.re.kr
SUMMARY:Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach” by R.C. Vendrell et.al.\, Sci. Adv. 2024 at the Journal Club. \nAbstract \nDe novo peptide design exhibits great potential in materials engineering\, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics—a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework’s effectiveness in generating peptide candidates\, evaluate its efficiency for property-oriented design\, and validate the candidates with molecular dynamics simulations. This quantum computing–based approach could accelerate the development of biomolecular tools for environmental and biomedical applications while advancing the study of biomolecular systems through quantum technologies. \n 
URL:https://www.ibs.re.kr/bimag/event/phantom-oscillations-in-principal-component-analysis-gyuyoung-hwang/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250321T110000
DTEND;TZID=Asia/Seoul:20250321T120000
DTSTAMP:20260422T131036
CREATED:20250217T075507Z
LAST-MODIFIED:20250217T075934Z
UID:10756-1742554800-1742558400@www.ibs.re.kr
SUMMARY:Disrupting Heathcare Using Deep Data and Remote Monitoring - Michael Snyder
DESCRIPTION:Abstract \nOur present healthcare system focuses on treating people when they are ill rather than keeping them healthy. We have been using big data and remote monitoring approaches to monitor people while they are healthy to keep them that way and detect disease at its earliest moment presymptomatically. We use advanced multiomics technologies (genomics\, immunomics\, transcriptomics\, proteomics\, metabolomics\, microbiomics) as well as wearables and microsampling for actively monitoring health. Following a group of 109 individuals for over 13 years revealed numerous major health discoveries covering cardiovascular disease\, oncology\, metabolic health and infectious disease. We have also found that individuals have distinct aging patterns that can be measured in an actionable period of time. Finally\, we have used wearable devices for early detection of infectious disease\, including COVID-19 as well as microsampling for monitoring and improving lifestyle. We believe that advanced technologies have the potential to transform healthcare and keep people healthy.
URL:https://www.ibs.re.kr/bimag/event/disrupting-heathcare-using-deep-data-and-remote-monitoring-michael-snyder/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/02/mike-snyder-e1739778881131.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250314T140000
DTEND;TZID=Asia/Seoul:20250314T160000
DTSTAMP:20260422T131036
CREATED:20250226T070011Z
LAST-MODIFIED:20250226T070011Z
UID:10806-1741960800-1741968000@www.ibs.re.kr
SUMMARY:A biological model of nonlinear dimensionality reduction - Shingo Gibo
DESCRIPTION:In this talk\, we discuss the paper “A biological model of nonlinear dimensionality reduction” by K. Yoshida and T. Toyoizumi\, Science Advances\, 2025\, at the Journal Club. \nAbstract \nObtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) have been developed\, their implementation in simple biological circuits remains unclear. Here\, we develop a biologically plausible dimensionality reduction algorithm compatible with t-SNE\, which uses a simple three-layer feedforward network mimicking the Drosophila olfactory circuit. The proposed learning rule\, described as three-factor Hebbian plasticity\, is effective for datasets such as entangled rings and MNIST\, comparable to t-SNE. We further show that the algorithm could be working in olfactory circuits in Drosophila by analyzing the multiple experimental data in previous studies. We lastly suggest that the algorithm is also beneficial for association learning between inputs and rewards\, allowing the generalization of these associations to other inputs not yet associated with rewards.
URL:https://www.ibs.re.kr/bimag/event/a-biological-model-of-nonlinear-dimensionality-reduction-shingo-gibo/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250314T110000
DTEND;TZID=Asia/Seoul:20250314T120000
DTSTAMP:20260422T131036
CREATED:20250217T075146Z
LAST-MODIFIED:20250217T075146Z
UID:10749-1741950000-1741953600@www.ibs.re.kr
SUMMARY:COVID-19 and Challenges to the Classical Theory of Epidemics - Simon Levin
DESCRIPTION:Abstract \nThe standard theory of infectious diseases\, tracing back to the work of Kermack and McKendrick nearly a century ago\, has been a triumph of mathematical biology\, a rare marriage of theory and application. Yet the limitations of its most simple representations\, which has always been known\, have been laid bare in dealing with COVID-19\, sparking a spate of extensions of the basic theory to deal more effectively with aspects of viral evolution\, asymptotic stages\, heterogeneity of various kinds\, the ambiguities of notions of herd immunity\, the role of social behaviors and other features. This lecture will address some progress in addressing these\, and open challenges in expanding the mathematical theory.
URL:https://www.ibs.re.kr/bimag/event/covid-19-and-challenges-to-the-classical-theory-of-epidemics-simon-levin/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/02/simon-levin-e1739778689468.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250307T140000
DTEND;TZID=Asia/Seoul:20250307T160000
DTSTAMP:20260422T131036
CREATED:20250226T065718Z
LAST-MODIFIED:20250305T000149Z
UID:10804-1741356000-1741363200@www.ibs.re.kr
SUMMARY:The Large Language Models on Biomedical Data Analysis: A Survey - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “The Large Language Models on Biomedical Data Analysis: A Survey” by Wei Lan et.al\, IEEE J. Biomedical and Health Informatics\, 2025\, at the Journal Club. \nAbstract  \nWith the rapid development of Large Language Model (LLM) technology\, it has become an indispensable force in biomedical data analysis research. However\, biomedical researchers currently have limited knowledge about LLM. Therefore\, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein\, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review\, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis\, followed by a detailed analysis of LLM applications in genomics\, proteomics\, transcriptomics\, radiomics\, single-cell analysis\, medical texts and drug discovery. Finally\, the challenges of LLM in biomedical data analysis are discussed. In summary\, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research.
URL:https://www.ibs.re.kr/bimag/event/machine-learning-model-for-menstrual-cycle-phase-classification-and-ovulation-day-detection-based-on-sleeping-heart-rate-under-free-living-conditions-myna-lim/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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