{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T13:14:46Z","timestamp":1723036486235},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: 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.<\/jats:p>\n               <jats:p>Results: 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.<\/jats:p>\n               <jats:p>Availability and implementation: The source code and some supporting files are available at https:\/\/github.com\/rabbitpei\/HMM_based-method.<\/jats:p>\n               <jats:p>Contacts: \u00a0lnchen@sibs.ac.cn or liyj@scut.edu.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw154","type":"journal-article","created":{"date-parts":[[2016,3,20]],"date-time":"2016-03-20T00:33:18Z","timestamp":1458433998000},"page":"2143-2150","source":"Crossref","is-referenced-by-count":48,"title":["Detecting critical state before phase transition of complex biological systems by hidden Markov model"],"prefix":"10.1093","volume":"32","author":[{"given":"Pei","family":"Chen","sequence":"first","affiliation":[{"name":"1 School of Computer Science and Engineering"}]},{"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"2 School of Mathematics, South China University of Technology, Guangzhou 510640, China"}]},{"given":"Yongjun","family":"Li","sequence":"additional","affiliation":[{"name":"1 School of Computer Science and Engineering"}]},{"given":"Luonan","family":"Chen","sequence":"additional","affiliation":[{"name":"3 Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China"},{"name":"4 Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan"}]}],"member":"286","published-online":{"date-parts":[[2016,3,19]]},"reference":[{"key":"2023020112452277500_btw154-B1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.nbd.2009.12.029","volume":"38","author":"Achiron","year":"2010","journal-title":"Neurobiol. 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