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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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DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211112T110000
DTEND;TZID=Asia/Seoul:20211112T120000
DTSTAMP:20260427T071204
CREATED:20211111T170000Z
LAST-MODIFIED:20211111T084242Z
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SUMMARY:Detecting and quantifying causal associations in large nonlinear time series datasets
DESCRIPTION:We will discuss about “Detecting and quantifying causal associations in large nonlinear time series datasets”\, Runge et al.\, Science Advances\, 2019 \nIdentifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here\, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power\, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.
URL:https://www.ibs.re.kr/bimag/event/2021-11-12-2/
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:20211118T130000
DTEND;TZID=Asia/Seoul:20211118T140000
DTSTAMP:20260427T071204
CREATED:20211117T190000Z
LAST-MODIFIED:20211101T080821Z
UID:5187-1637240400-1637244000@www.ibs.re.kr
SUMMARY:Solving Singular Control Problems in Mathematical Biology\, Using PASA
DESCRIPTION:We will discuss about “Solving Singular Control Problems in Mathematical Biology\, Using PASA”\, Atkins et al.\, arXiv\, 2020 \nIn this paper\, we will demonstrate how to use a nonlinear polyhedral constrained optimization solver called the Polyhedral Active Set Algorithm (PASA) for solving a general singular control problem. We present methods of discretizing a general optimal control problem that involves the use of the gradient of the Lagrangian for computing the gradient of the cost functional so that PASA can be applied. When a numerical solution contains artifacts that resemble “chattering”\, a phenomenon where the control oscillates wildly along the singular region\, we recommend a method of regularizing the singular control problem by adding a term to the cost functional that measures a scalar multiple of the total variation of the control\, where the scalar is viewed as a tuning parameter. We then demonstrate PASA’s performance on three singular control problems that give rise to different applications of mathematical biology. We also provide some exposition on the heuristics that we use in determining an appropriate size for the tuning parameter.
URL:https://www.ibs.re.kr/bimag/event/2021-11-18-2/
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:20211126T100000
DTEND;TZID=Asia/Seoul:20211126T110000
DTSTAMP:20260427T071204
CREATED:20211124T190000Z
LAST-MODIFIED:20211122T014405Z
UID:5190-1637920800-1637924400@www.ibs.re.kr
SUMMARY:A Random Matrix Theory Approach to Denoise Single-Cell Data
DESCRIPTION:We will discuss about “A Random Matrix Theory Approach to Denoise Single-Cell Data”\, Aparicio et al.\, Patterns\, 2020 \nSingle-cell technologies provide the opportunity to identify new cellular states. However\, a major obstacle to the identification of biological signals is noise in single-cell data. In addition\, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition\, we explain how sparsity can cause spurious eigenvector localization\, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory\, about 3% is spurious signal induced by sparsity\, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations.
URL:https://www.ibs.re.kr/bimag/event/a-random-matrix-theory-approach-to-denoise-single-cell-data/
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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