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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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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250829T140000
DTEND;TZID=Asia/Seoul:20250829T160000
DTSTAMP:20260422T093001
CREATED:20250727T024418Z
LAST-MODIFIED:20250727T024418Z
UID:11348-1756476000-1756483200@www.ibs.re.kr
SUMMARY:Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains - Jinwoo Hyun
DESCRIPTION:In this talk\, we discuss the paper “Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains” by K. Lee. \nAbstract \nThe ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases\, considering the inherent properties of datasets. In tabular domains\, it is critical to effectively handle heterogeneous features (both categorical and numerical) in a unified manner and to grasp irregular functions like piecewise constant functions. To address the challenges in the self-supervised learning framework\, we propose a novel pretext task based on the classical binning method. The idea is straightforward: reconstructing the bin indices (either orders or classes) rather than the original values. This pretext task provides the encoder with an inductive bias to capture the irregular dependencies\, mapping from continuous inputs to discretized bins\, and mitigates the feature heterogeneity by setting all features to have category-type targets. Our empirical investigations ascertain several advantages of binning: compatibility with encoder architecture and additional modifications\, standardizing all features into equal sets\, grouping similar values within a feature\, and providing ordering information. Comprehensive evaluations across diverse tabular datasets corroborate that our method consistently improves tabular representation learning performance for a wide range of downstream tasks. The codes are available in the supplementary material.
URL:https://www.ibs.re.kr/bimag/event/binning-as-a-pretext-task-improving-self-supervised-learning-in-tabular-domains-jinwoo-hyun/
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:20250822T153000
DTEND;TZID=Asia/Seoul:20250822T173000
DTSTAMP:20260422T093001
CREATED:20250803T065046Z
LAST-MODIFIED:20250819T002937Z
UID:11366-1755876600-1755883800@www.ibs.re.kr
SUMMARY:Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters” by L. Xia et.al. Nature Communications\, 2024. \nAbstract \nTwo-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however\, it is well known that t-SNE and UMAP’s 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge\, we present a statistical method\, scDEED\, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell’s 2D-embedding neighbors and pre-embedding neighbors\, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover\, by minimizing the number of dubious cell embeddings\, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.
URL:https://www.ibs.re.kr/bimag/event/context-aware-deconvolution-of-cell-cell-communication-with-tensor-cell2cell-kevin-spinicci/
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:20250808T140000
DTEND;TZID=Asia/Seoul:20250808T160000
DTSTAMP:20260422T093001
CREATED:20250727T024732Z
LAST-MODIFIED:20250727T024732Z
UID:11351-1754661600-1754668800@www.ibs.re.kr
SUMMARY:Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan” by J. Shim et.al.\, npj digital medicine\, 2024. \nAbstract \nRecognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables\, we introduce CosinorAge\, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80\,000 midlife and older adults in the UK and US. A one-year increase in\nCosinorAge corresponded to 8–12% higher all-cause and cause-specific mortality risks and 3–14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning\, evidenced by an 8–33% reduction in self-rated health\nand a 3–23% decline in health-related quality of life score\, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover\, we\nillustrated a heterogeneous impact of circadian parameters associated with biological aging\, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers\, diminishing to a 7-minute difference in older adults\n(>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge’s potential as a scalable\, economic\, and digital solution for promoting healthy longevity\, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently\, our research contributes to advancing preventive measures in digital medicine.
URL:https://www.ibs.re.kr/bimag/event/circadian-rhythm-analysis-using-wearable-based-accelerometry-as-a-digital-biomarker-of-aging-and-healthspan-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:20250801T140000
DTEND;TZID=Asia/Seoul:20250801T160000
DTSTAMP:20260422T093001
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:20260422T093001
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:20250718T140000
DTEND;TZID=Asia/Seoul:20250718T160000
DTSTAMP:20260422T093001
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:20260422T093001
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:20260422T093001
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:20250627T140000
DTEND;TZID=Asia/Seoul:20250627T160000
DTSTAMP:20260422T093001
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:20260422T093001
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:20260422T093001
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:20260422T093001
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:20250509T140000
DTEND;TZID=Asia/Seoul:20250509T160000
DTSTAMP:20260422T093001
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:20260422T093001
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:20250418T140000
DTEND;TZID=Asia/Seoul:20250418T160000
DTSTAMP:20260422T093001
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:20260422T093001
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:20260422T093001
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:20250328T140000
DTEND;TZID=Asia/Seoul:20250328T160000
DTSTAMP:20260422T093001
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:20250321T143000
DTEND;TZID=Asia/Seoul:20250321T163000
DTSTAMP:20260422T093001
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:20250314T140000
DTEND;TZID=Asia/Seoul:20250314T160000
DTSTAMP:20260422T093001
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:20250307T140000
DTEND;TZID=Asia/Seoul:20250307T160000
DTSTAMP:20260422T093001
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250228T140000
DTEND;TZID=Asia/Seoul:20250228T160000
DTSTAMP:20260422T093001
CREATED:20250220T082847Z
LAST-MODIFIED:20250225T080719Z
UID:10787-1740751200-1740758400@www.ibs.re.kr
SUMMARY:Quantifying information accumulation encoded in the dynamics of biochemical signaling - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Quantifying information accumulation encoded in the dynamics of biochemical signaling” by Y. Tang\, et.al\, Nature Communications\, 2021. \nAbstract \nCellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However\, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is\, in part\, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here\, we develop a quantitative framework\, based on inferred trajectory probabilities\, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NFκB transcriptional dynamics in response to different immune threats\, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the IκBα negative feedback circuit. The framework is generally applicable to single-cell time series measurements\, and enables understanding how temporal regulatory codes transmit information over time.
URL:https://www.ibs.re.kr/bimag/event/quantum-computing-enhanced-algorithm-unveils-potential-kras-inhibitors-kang-min-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:20250221T140000
DTEND;TZID=Asia/Seoul:20250221T160000
DTSTAMP:20260422T093001
CREATED:20250128T024716Z
LAST-MODIFIED:20250203T004930Z
UID:10712-1740146400-1740153600@www.ibs.re.kr
SUMMARY:Constraining nonlinear time series modeling with the metabolic theory of ecology - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Constraining nonlinear time series modeling with the metabolic theory of ecology” by S.B. Munch et.al.\, PNAS\, 2023. \nAbstract \nForecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature\, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM)\, an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step\,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average)\, with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate\, rather than the form\, of population dynamics\, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable\, at least approximately\, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.
URL:https://www.ibs.re.kr/bimag/event/constraining-nonlinear-time-series-modeling-with-the-metabolic-theory-of-ecology-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:20250214T140000
DTEND;TZID=Asia/Seoul:20250214T160000
DTSTAMP:20260422T093001
CREATED:20250128T024512Z
LAST-MODIFIED:20250203T004838Z
UID:10710-1739541600-1739548800@www.ibs.re.kr
SUMMARY:Method for cycle detection in sparse\, irregularly sampled\, long-term neuro-behavioral timeseries - Brenda Gavina
DESCRIPTION:In this talk\, we discuss the paper “Method for cycle detection in sparse\, irregularly sampled\, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term\, inter-ictal epileptiform activity” by Irena Balzekas et.al.\, Plos Com.\, 2024. \nAbstract \nNumerous physiological processes are cyclical\, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep\, wakefulness\, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans\, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases\, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals.
URL:https://www.ibs.re.kr/bimag/event/method-for-cycle-detection-in-sparse-irregularly-sampled-long-term-neuro-behavioral-timeseries-brenda-gavina/
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:20250207T140000
DTEND;TZID=Asia/Seoul:20250207T160000
DTSTAMP:20260422T093001
CREATED:20250128T024238Z
LAST-MODIFIED:20250206T103822Z
UID:10708-1738936800-1738944000@www.ibs.re.kr
SUMMARY:A cell atlas foundation model for scalable search of similar human cells - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “A cell atlas foundation model for scalable search of similar human cells” by Graham Heimberg et.al.\, Nature\, 2024 at the Journal Club. \nAbstract \n\n\nSingle-cell RNA sequencing has profiled hundreds of millions of human cells across organs\, diseases\, development and perturbations to date. Mining these growing atlases could reveal cell–disease associations\, identify cell states in unexpected tissue contexts and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here we develop SCimilarity\, a metric-learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4-million-cell atlas of 412 single-cell RNA-sequencing studies for macrophage and fibroblast profiles from interstitial lung disease1 and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system2\, which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body\, providing a powerful tool for generating biological insights from the Human Cell Atlas.
URL:https://www.ibs.re.kr/bimag/event/scdiffusion-conditional-generation-of-high-quality-single-cell-data-using-diffusion-model-kevin-spinicci/
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:20250131T140000
DTEND;TZID=Asia/Seoul:20250131T160000
DTSTAMP:20260422T093001
CREATED:20250126T021153Z
LAST-MODIFIED:20250203T004702Z
UID:10696-1738332000-1738339200@www.ibs.re.kr
SUMMARY:Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Self-supervised learning of accelerometer data provides new insights for sleep and\nits association with mortality” by H. Yuan et.al\, npj digital medicine\, 2024\, at the Journal Club. \nAbstract  \nSleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus\, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion\, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration\, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with ~100\,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66\,262 UK Biobank participants\, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6–7.9 h\, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04–1.61). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.
URL:https://www.ibs.re.kr/bimag/event/self-supervised-learning-of-accelerometer-data-provides-new-insights-for-sleep-and-its-association-with-mortality-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:20250124T140000
DTEND;TZID=Asia/Seoul:20250124T160000
DTSTAMP:20260422T093001
CREATED:20250104T005711Z
LAST-MODIFIED:20250104T005711Z
UID:10531-1737727200-1737734400@www.ibs.re.kr
SUMMARY:Plausible\, robust biological oscillations through allelic buffering - Eui Min Jeong
DESCRIPTION:In this talk\, we discuss the paper “Plausible\, robust biological oscillations through allelic buffering” by F-S. Hsieh et.al\, Cell Systems\, 2024. at the Journal Club.  \nAbstract \nBiological oscillators can specify time- and dose-dependent functions via dedicated control of their oscillatory dynamics. However\, how biological oscillators\, which recurrently activate noisy biochemical processes\, achieve robust oscillations remains unclear. Here\, we characterize the long-term oscillations of p53 and its negative feedback regulator Mdm2 in single cells after DNA damage. Whereas p53 oscillates regularly\, Mdm2 from a single MDM2 allele exhibits random unresponsiveness to ∼9% of p53 pulses. Using allelic-specific imaging of MDM2 activity\, we show that MDM2 alleles buffer each other to maintain p53 pulse amplitude. Removal of MDM2 allelic buffering cripples the robustness of p53 amplitude\, thereby elevating p21 levels and cell-cycle arrest. In silico simulations support that allelic buffering enhances the robustness of biological oscillators and broadens their plausible biochemical space. Our findings show how allelic buffering ensures robust p53 oscillations\, highlighting the potential importance of allelic buffering for the emergence of robust biological oscillators during evolution. A record of this paper’s transparent peer review process is included in the supplemental information. 
URL:https://www.ibs.re.kr/bimag/event/plausible-robust-biological-oscillations-through-allelic-buffering-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:20250110T140000
DTEND;TZID=Asia/Seoul:20250110T160000
DTSTAMP:20260422T093001
CREATED:20250104T003730Z
LAST-MODIFIED:20250107T122054Z
UID:10529-1736517600-1736524800@www.ibs.re.kr
SUMMARY:CARE as a wearable derived feature linking circadian amplitude to human cognitive functions - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “CARE as a wearable derived feature linking circadian amplitude to human cognitive functions” by Shuya Cui et.al.\, npj Digital Medicine\, 2023. \nAbstract \nCircadian rhythms are crucial for regulating physiological and behavioral processes. Pineal hormone melatonin is often used to measure circadian amplitude but its collection is costly and time-consuming. Wearable activity data are promising alternative\, but the most commonly used measure\, relative amplitude\, is subject to behavioral masking. In this study\, we firstly derive a feature named circadian activity rhythm energy (CARE) to better characterize circadian amplitude and validate CARE by correlating it with melatonin amplitude (Pearson’s r = 0.46\, P = 0.007) among 33 healthy participants. Then we investigate its association with cognitive functions in an adolescent dataset (Chinese SCHEDULE-A\, n = 1703) and an adult dataset (UK Biobank\, n = 92\,202)\, and find that CARE is significantly associated with Global Executive Composite (β = 30.86\, P = 0.016) in adolescents\, and reasoning ability\, short-term memory\, and prospective memory (OR = 0.01\, 3.42\, and 11.47 respectively\, all P < 0.001) in adults. Finally\, we identify one genetic locus with 126 CARE-associated SNPs using the genome-wide association study\, of which 109 variants are used as instrumental variables in the Mendelian Randomization analysis\, and the results show a significant causal effect of CARE on reasoning ability\, short-term memory\, and prospective memory (β = -59.91\, 7.94\, and 16.85 respectively\, all P < 0.0001). The present study suggests that CARE is an effective wearable-based metric of circadian amplitude with a strong genetic basis and clinical significance\, and its adoption can facilitate future circadian studies and potential intervention strategies to improve circadian rhythms and cognitive functions.
URL:https://www.ibs.re.kr/bimag/event/mapping-the-physiological-changes-in-sleep-regulation-across-infancy-and-young-childhood-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:20250103T140000
DTEND;TZID=Asia/Seoul:20250103T160000
DTSTAMP:20260422T093001
CREATED:20250101T061847Z
LAST-MODIFIED:20250101T061847Z
UID:10503-1735912800-1735920000@www.ibs.re.kr
SUMMARY:Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model - Seokhwan Moon
DESCRIPTION:In this talk\, we discuss the paper “Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model” by F. W. Townes et.al.\, Genome Biology\, 2019. \nAbstract  \nSingle-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls\, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods\, including generalized principal component analysis (GLM-PCA) for non-normal distributions\, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.
URL:https://www.ibs.re.kr/bimag/event/feature-selection-and-dimension-reduction-for-single-cell-rna-seq-based-on-a-multinomial-model-seokhwan-moon/
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:20241220T140000
DTEND;TZID=Asia/Seoul:20241220T160000
DTSTAMP:20260422T093001
CREATED:20241209T001156Z
LAST-MODIFIED:20241219T012147Z
UID:10339-1734703200-1734710400@www.ibs.re.kr
SUMMARY:cellFlow: a generative flow-based model for single-cell count data - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “cellFlow: a generative flow-based model for single-cell count data” by A. Palma et.al\, ICLR\, 2024. \nAbstract  \nGenerative modeling for single-cell RNA-seq has proven transformative in crucial fields such as learning single-cell representations and perturbation responses. However\, despite their appeal in relevant applications involving data augmentation and unseen cell state prediction\, use cases like generating artificial biological samples are still in their pioneering phase. While common approaches producing single-cell samples from noise operate in continuous space by assuming normalized gene expression\, we argue for the necessity of sample generation in a raw transcription count space to favor processing-agnostic data generation and flexible downstream applications. To this end\, we propose cellFlow\, a Flow-Matching-based model that generates single-cell count data. In our empirical study\, cellFlow performs on par with existing methods operating on normalized data when evaluated on three biological datasets. By carefully considering raw single-cell distributional properties\, cellFlow is a promising avenue for future developments in single-cell generative models.
URL:https://www.ibs.re.kr/bimag/event/qclus-a-droplet-filtering-algorithm-for-enhanced-snrna-seq-data-quality-in-challenging-samples-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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END:VCALENDAR