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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:20221118T150000
DTEND;TZID=Asia/Seoul:20221118T170000
DTSTAMP:20260426T082731
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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