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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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METHOD:PUBLISH
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:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210701T110000
DTEND;TZID=Asia/Seoul:20210701T120000
DTSTAMP:20260502T191134
CREATED:20210603T003009Z
LAST-MODIFIED:20210604T082929Z
UID:4605-1625137200-1625140800@www.ibs.re.kr
SUMMARY:Statistical Inference with Neural Network Imputation for Item Nonresponse
DESCRIPTION:Abstract: We consider the problem of nonparametric imputation using neural network models. Neural network models can capture complex nonlinear trends and interaction effects\, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation\, including variance estimation\, is challenging because the basis for function estimation is estimated rather than known. In this paper\, we tackle the problem of statistical inference with neural network imputation by treating the hidden nodes in a neural network as data-driven basis functions. We prove that the uncertainty in estimating the basis functions can be safely ignored and hence the linearization method for neural network imputation can be greatly simplified. A simulation study confirms that the proposed approach results in efficient and well-calibrated confidence intervals even when classic approaches fail due to severe nonlinearity and complicated interactions.
URL:https://www.ibs.re.kr/bimag/event/2021-07-01/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/06/JKK_profile2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210712T100000
DTEND;TZID=Asia/Seoul:20210712T120000
DTSTAMP:20260502T191134
CREATED:20210617T030615Z
LAST-MODIFIED:20210617T030615Z
UID:4658-1626084000-1626091200@www.ibs.re.kr
SUMMARY:Analysis of sleep-wake cycles via machine learning and mathematical modeling
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2021-07-21/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20210723T110000
DTEND;TZID=Asia/Seoul:20210723T120000
DTSTAMP:20260502T191134
CREATED:20210707T160416Z
LAST-MODIFIED:20210707T160416Z
UID:4715-1627038000-1627041600@www.ibs.re.kr
SUMMARY:Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC
DESCRIPTION:Abstract: Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC In this study\, we discuss model robustness. Model robustness is consistent performance over variations of parameters. We formulate a stochastic target-mediated drug (TMDD) model\, one of the pharmacokinetic models\, to capture bi-exponential drug decay in plasma. A stochastic process is used to account for system randomness\, and this process is transformed into system of stochastic differential equations. Parameter inference is performed by Approximation Bayesian Computation using the likelihood-free method. Using these collected samples\, global sensitivity of parameters is compared to Uniform and Normal distributions. This approach in the TMDD model may improve model robustness without changing the global sensitivity of parameters and the model.
URL:https://www.ibs.re.kr/bimag/event/inference-method-for-a-stochastic-target-mediated-drug-disposition-model-via-abc-mcmc/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20210723T150000
DTEND;TZID=Asia/Seoul:20210723T160000
DTSTAMP:20260502T191134
CREATED:20210629T013222Z
LAST-MODIFIED:20210629T013222Z
UID:4686-1627052400-1627056000@www.ibs.re.kr
SUMMARY:Scalable Modeling Approaches in Systems Immunology
DESCRIPTION:Abstract: \nSystems biology seeks to build quantitative predictive models of biological system behavior. Biological systems\, such as the mammalian immune system\, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus\, mechanistic\, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with the high-dimensional parameter space arising when building reasonably detailed models. Another challenge is to devise integrated frameworks incorporating behavioral characteristics manifested at various organizational levels seamlessly. First\, we aimed to understand how cell-to-cell heterogeneities are regulated through gene expression variations and their propagation at the single-cell level. To better understand detailed gene regulatory circuit models with many parameters without analytical solutions\, we developed a framework called MAchine learning of Parameter-Phenotype Analysis (MAPPA). MAPPA combines machine learning approaches and stochastic simulation methods to dissect the mapping between high-dimensional parameters and phenotypes. MAPPA elucidated regulatory features of stochastic gene-gene correlation phenotypes. Next\, we sought to quantitatively dissect immune homeostasis conferring tolerance to self-antigens and responsiveness to foreign antigens. Towards this goal\, we built a series of models spanning from intracellular to organismal levels to describe the recurrent reciprocal relationships between self-reactive T cells and regulatory T cells in collaboration with an experimentalist. This effort elucidated critical immune parameters regulating the circuitry enabling the robust suppression of self-reactive T cells\, followed by experimental validation. Moreover\, by bridging these models across organizational scales\, we derived a framework describing immune homeostasis as a dynamical equilibrium between self-activated T cells and regulatory T cells\, typically operating well below thresholds that could result in clonal expansion and subsequent autoimmune diseases. We propose that our framework and predictions may help guide therapeutic manipulation of immune homeostasis to treat cancer and autoimmune diseases. \n  \nReferences: \nPark\, K.\, Prüstel\, T.\, Lu\, Y.\, and Tsang\, J.S. (2019). Machine learning of stochastic gene network phenotypes. BioRxiv 825943. \nWong\, H.S.\, Park\, K.\, Gola\, A.\, Baptista\, A.P.\, Miller\, C.H.\, Deep\, D.\, Lou\, M.\, Boyd\, L.F.\, Rudensky\, A.Y.\, Savage\, P.A.\, et al. (2021). A local regulatory T cell feedback circuit maintains immune homeostasis by pruning self-activated T cells. Cell S0092867421006589.
URL:https://www.ibs.re.kr/bimag/event/2021-07-23/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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