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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:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231110T140000
DTEND;TZID=Asia/Seoul:20231110T160000
DTSTAMP:20260425T071045
CREATED:20231030T040327Z
LAST-MODIFIED:20231109T000051Z
UID:8661-1699624800-1699632000@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning
DESCRIPTION:We will discuss about “Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning.” bioRxiv (2023): 2023-09. \n  \nAbstract \nThe recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable\, the CRNN acts as a digital twin of a classical chemical reaction network. In this study\, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this\, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework\, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally\, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together\, this provides a new framework for robust\, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2023-11-10-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20231124T140000
DTEND;TZID=Asia/Seoul:20231124T160000
DTSTAMP:20260425T071045
CREATED:20231030T040641Z
LAST-MODIFIED:20231114T081738Z
UID:8663-1700834400-1700841600@www.ibs.re.kr
SUMMARY:Dongju Lim\, An accurate probabilistic step finder for time-series analysis
DESCRIPTION:We will discuss about “An accurate probabilistic step finder for time-series analysis.” bioRxiv (2023): 2023-09. \nAbstract \n\n\n\n\nNoisy time-series data is commonly collected from sources including Förster Resonance Energy Transfer experiments\, patch clamp and force spectroscopy setups\, among many others. Two of the most common paradigms for the detection of discrete transitions in such time-series data include: hidden Markov models (HMMs) and step-finding algorithms. HMMs\, including their extensions to infinite state-spaces\, inherently assume in analysis that holding times in discrete states visited are geometrically–or\, loosely speaking in common language\, exponentially–distributed. Thus the determination of step locations\, especially in sparse and noisy data\, is biased by HMMs toward identifying steps resulting in geometric holding times. In contrast\, existing step-finding algorithms\, while free of this restraint\, often rely on ad hoc metrics to penalize steps recovered in time traces (by using various information criteria) and otherwise rely on approximate greedy algorithms to identify putative global optima. Here\, instead\, we devise a robust and general probabilistic (Bayesian) step-finding tool that neither relies on ad hoc metrics to penalize step numbers nor assumes geometric holding times in each state. As the number of steps themselves in a time-series are\, a priori unknown\, we treat these within a Bayesian nonparametric (BNP) paradigm. We find that the method developed\, Bayesian Nonparametric Step (BNP-Step)\, accurately determines the number and location of transitions between discrete states without any assumed kinetic model and learns the emission distribution characteristic of each state. In doing so\, we verify that BNP-Step can analyze sparser data sets containing higher noise and more closely-spaced states than otherwise resolved by current state-of-the-art methods. What is more\, BNP-Step rigorously propagates measurement uncertainty into uncertainty over state transition locations\, numbers\, and emission levels as characterized by the posterior. We demonstrate the performance of BNP-Step on both synthetic data as well as data drawn from force spectroscopy experiments. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-11-24-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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