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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
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DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220616T130000
DTEND;TZID=Asia/Seoul:20220616T140000
DTSTAMP:20260425T070726
CREATED:20220615T190000Z
LAST-MODIFIED:20220623T060231Z
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SUMMARY:Identifying the critical states of complex diseases by the dynamic change of multivariate distribution
DESCRIPTION:We will discuss about “Identifying the critical states of complex diseases by the dynamic change of multivariate distribution”\, Peng\, Hao\, et al.\, Briefings in Bioinformatics\, 2022. \nAbstract: The dynamics of complex diseases are not always smooth; they are occasionally abrupt\, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements\, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study\, we propose a computational method\, the direct interaction network-based divergence\, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets\, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.
URL:https://www.ibs.re.kr/bimag/event/2022-06-16-jc-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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220616T160000
DTEND;TZID=Asia/Seoul:20220616T170000
DTSTAMP:20260425T070726
CREATED:20220613T130628Z
LAST-MODIFIED:20220613T130628Z
UID:6180-1655395200-1655398800@www.ibs.re.kr
SUMMARY:Deep Learning-based Uncertainty Quantification for Mathematical Models
DESCRIPTION:Over the recent years\, various methods based on deep neural networks have been developed and utilized in a wide range of scientific fields. Deep neural networks are highly suitable for analyzing time series or spatial data with complicated dependence structures\, making them particularly useful for environmental sciences and biosciences where such type of simulation model output and observations are prevalent. In this talk\, I will introduce my recent efforts in utilizing various deep learning methods for statistical analysis of mathematical simulations and observational data in those areas\, including surrogate modeling\, parameter estimation\, and long-term trend reconstruction. Various scientific application examples will also be discussed\, including ocean diffusivity estimation\, WRF-hydro calibration\, AMOC reconstruction\, and SIR calibration.  
URL:https://www.ibs.re.kr/bimag/event/2022-06-13-seminar-wonchang/
LOCATION:B378 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
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