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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:20210813T090000
DTEND;TZID=Asia/Seoul:20210813T100000
DTSTAMP:20260510T080936
CREATED:20210810T220000Z
LAST-MODIFIED:20210811T064522Z
UID:4837-1628845200-1628848800@www.ibs.re.kr
SUMMARY:TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data
DESCRIPTION:We will discuss about “TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data”\, Ness-Cohn and Braun\, Bioinformatics\, 2021 \nAbstract \nMotivation: The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics\, coupled with the significant implicatins of the circadian clock for human health\, has sparked an interest in circadian profiling studies to discover genes under circadian control.\nResult: We present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series\, the method reconstructs the state space using time-delay embedding\, a data transformation technique from dynamical systems theory. In the embedded space\, Takens’ theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology\, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution\, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle’s ability to identify cycling genes across a range of sampling schemes\, number of replicates\, and missing data. Comparison to competing methods highlights their relative strengths\, providing guidance as to the optimal choice of cycling detection method.\nAvailability: A fully documented open-source R package implementing TimeCycle is available at: https://nesscoder.github.io/TimeCycle/ .
URL:https://www.ibs.re.kr/bimag/event/2021-08-13-2/
LOCATION:B305 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:20210813T110000
DTEND;TZID=Asia/Seoul:20210813T120000
DTSTAMP:20260510T080936
CREATED:20210727T190000Z
LAST-MODIFIED:20210731T015814Z
UID:4750-1628852400-1628856000@www.ibs.re.kr
SUMMARY:Bayesian model calibration and sensitivity analysis for oscillating biochemical experiments
DESCRIPTION:Abstract: Most organisms exhibit various endogenous oscillating behaviors\, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments\, studying such periodicity of organisms often utilizes computer experiments relying on ordinary differential equations (ODE) because configuring the internal processes is difficult. Simultaneously utilizing both experiments\, however\, poses a significant statistical challenge due to its ill behavior in high dimension\, identifiability\, and numerical instability. This article devises a new Bayesian calibration strategy for oscillating biochemical models. The proposed methodology can efficiently estimate the computer experiments’ (ODE) parameters that match the physical experiments. The proposed framework is illustrated with circadian oscillations observed in a model filamentous fungus\, Neurospora crassa.
URL:https://www.ibs.re.kr/bimag/event/2021-08-13/
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/2021/07/HJK_profile-e1626653369732.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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