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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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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221118T110000
DTEND;TZID=Asia/Seoul:20221118T120000
DTSTAMP:20260425T094952
CREATED:20220825T012410Z
LAST-MODIFIED:20221114T224951Z
UID:6490-1668769200-1668772800@www.ibs.re.kr
SUMMARY:Quantifying dynamical changes in sparse\, noisy\, high-dimensional data
DESCRIPTION:The circadian clock orchestrates a vast array of behavioral and physiological processes with a 24-hour cycle\, enabling nearly all organisms — from bread mold to fruit-flies to humans — to anticipate and adapt to the Earth’s day. Entrainable by environmental cue\, the rhythm itself is generated by a self-sustained molecular oscillator present in nearly every cell. This in turn governs the expression of thousands of genes\, precisely coordinating biomolecular functions at the microscopic scale. While experimental evidence suggests that the clock is crucial for mediating the response to changes in an organism’s environment (such as temperature and food availability)\, the precise mechanisms underlying circadian regulation remain unclear. Today\, high-throughput omics assays enable us to probe these processes in molecular detail\, with the goal of making inferences about which genes are under circadian control and how their dynamics change under different environmental conditions. Analyzing this transcriptomic time-series data raises new challenges: that of characterizing dynamics when the data are noisy\, sparsely sampled in time\, and may not be strictly periodic. In this talk\, I will discuss our recent work on nonparametric methods to analyze circadian transcriptomic data by exploiting results from dynamical systems theory\, nonlinear dimension reduction\, and topological data analysis.
URL:https://www.ibs.re.kr/bimag/event/2022-11-18-colloquium/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/08/braun_rosemary.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221118T150000
DTEND;TZID=Asia/Seoul:20221118T170000
DTSTAMP:20260425T094952
CREATED:20221117T034958Z
LAST-MODIFIED:20221117T034958Z
UID:6871-1668783600-1668790800@www.ibs.re.kr
SUMMARY:Detecting critical state before phase transition of complex biological systems by hidden Markov model
DESCRIPTION:We will discuss about “Detecting critical state before phase transition of complex biological systems by hidden Markov model”\, Chen\, Pei\, et al. Bioinformatics 32.14 (2016): 2143-2150. \n  \nAbstract \nMotivation: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task\, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages\, i.e. before-transition state\, pre-transition state and after-transition state\, which can be considered as three different Markov processes. \nResults: By exploring the rich dynamical information provided by high-throughput data\, we present a novel computational method\, i.e. hidden Markov model (HMM) based approach\, to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process)\, thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness\, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets\, and further identify the pre-transition states as well as their critical modules for three real datasets\, i.e. the acute lung injury triggered by phosgene inhalation\, MCF-7 human breast cancer caused by heregulin and HCV-induced dysplasia and hepatocellular carcinoma. Both functional and pathway enrichment analyses validate the computational results.
URL:https://www.ibs.re.kr/bimag/event/2022-11-18-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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