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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230106T150000
DTEND;TZID=Asia/Seoul:20230106T170000
DTSTAMP:20260426T030330
CREATED:20221227T081429Z
LAST-MODIFIED:20230102T121025Z
UID:7178-1673017200-1673024400@www.ibs.re.kr
SUMMARY:Aurelio A. de los Reyes V\, Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
DESCRIPTION:We will discuss about “Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems”\, Linka\, Kevin\, et al.\, Computer Methods in Applied Mechanics and Engineering Volume 402\, 1 December 2022\, 115346 \nAbstract \n\n\n\n\nUnderstanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines\, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems\, identify patterns in big data\, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However\, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data\, physics\, and uncertainties by combining neural networks\, physics informed modeling\, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system\, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics\, robustly solve forward and inverse problems\, and perform well for both interpolation and extrapolation\, even for a small amount of noisy and incomplete data. At only minor additional cost\, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference\, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks\, Bayesian Inference\, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease\, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and\, more broadly\, to a wide variety of nonlinear dynamical systems. Our source code and examples are available at https://github.com/LivingMatterLab/xPINNs.
URL:https://www.ibs.re.kr/bimag/event/2023-01-06-jc/
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:20230112T130000
DTEND;TZID=Asia/Seoul:20230112T150000
DTSTAMP:20260426T030330
CREATED:20221228T005748Z
LAST-MODIFIED:20230108T080223Z
UID:7183-1673528400-1673535600@www.ibs.re.kr
SUMMARY:Hyun Kim\, Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics
DESCRIPTION:We will discuss about “Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics”\n\, Lin\, Baihan.\, arXiv preprint arXiv:2204.14048 (2022). \nAbstract \n\nThe absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate\, differentiate\, and compete\, i.e. the cell ecology. With the recent advancement of the single-cell RNA-sequencing (RNA-seq)\, we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs\, and analyzing these graphs systematically using the summary statistics informed by the algebraic topology. We propose the single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks\, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38\,731 cells\, 25 cell types and 12 time steps\, our approach highlights the gastrulation as the most critical stage\, consistent with consensus in developmental biology. As a nonlinear\, model-independent\, and unsupervised framework\, our approach can also be applied to tracing multi-scale cell lineage\, identifying critical stages\, or creating pseudo-time series.
URL:https://www.ibs.re.kr/bimag/event/2023-01-13-jc/
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:20230120T110000
DTEND;TZID=Asia/Seoul:20230120T130000
DTSTAMP:20260426T030330
CREATED:20221228T011141Z
LAST-MODIFIED:20221228T011141Z
UID:7186-1674212400-1674219600@www.ibs.re.kr
SUMMARY:Yun Min Song\, A scalable approach for solving chemical master equations based on modularization and filtering
DESCRIPTION:We will discuss about “A scalable approach for solving chemical master equations based on modularization and filtering\n”\, Fang\, Zhou\, Ankit Gupta\, and Mustafa Khammash.\, bioRxiv (2022). \nAbstract \n\nSolving the chemical master equation (CME) that characterizes the probability evolution of stochastically reacting processes is greatly important for analyzing intracellular reaction systems. Conventional methods for solving CMEs include the simulation-based Monte-Carlo methods\, the direct approach (e.g.\, the finite state projection)\, and so on; however\, they usually do not scale very well with the system dimension either in terms of accuracy or efficiency. To mitigate this problem\, we propose a new computational method based on modularization and filtering. Our method first divides the whole system into a leader system and several conditionally independent follower subsystems. Then\, we solve the CME by applying the Monte Carlo method to the leader system and the direct approach to the filtered CMEs that characterize the conditional probabilities of the follower subsystems. The system decomposition involved in our method is optimized so that all the subproblems above are low dimensional\, and\, therefore\, our approach scales more favorably with the system dimension. Finally\, we demonstrate the efficiency and accuracy of our approach in high-dimensional estimation and inference problems using several biologically relevant examples.
URL:https://www.ibs.re.kr/bimag/event/2023-01-20-jc/
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:20230127T110000
DTEND;TZID=Asia/Seoul:20230127T130000
DTSTAMP:20260426T030330
CREATED:20221227T081814Z
LAST-MODIFIED:20230126T010049Z
UID:7180-1674817200-1674824400@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Optimal information networks: Application for data-driven integrated health in populations
DESCRIPTION:We will discuss about “Optimal information networks: Application for data-driven integrated health in populations”\, Servadio\, Joseph L.\, and Matteo Convertino\, Science Advances 4.2 (2018): e1701088. \nAbstract \n\n\n\nDevelopment of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free\, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables\, instead considering only individual correlations. In addition\, a unified method for assessing integrated health statuses of populations is lacking\, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets\, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator\, representing integrated health in a city.
URL:https://www.ibs.re.kr/bimag/event/2023-01-27-jc/
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
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