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The sudden deterioration of diseases can be viewed as a bifurcation or a critical transition. Therefore, hunting for the tipping point or critical state is of great importance to prevent the disease deterioration. However, it is still a challenging task to detect the critical states of complex diseases with high-dimensional data, especially based on an individual. In this study, we develop a new method based on network fluctuation of molecules, temporal network flow entropy (TNFE) or temporal differential network flow entropy, to detect the critical states of complex diseases on the basis of each individual. By applying this method to a simulated dataset and six real diseases, including respiratory viral infections and tumors with four time-course and two stage-course high-dimensional omics datasets, the critical states before deterioration were detected and their dynamic network biomarkers were identified successfully. The results on the simulated dataset indicate that the TNFE method is robust under different noise strengths, and is also superior to the existing methods on detecting the critical states. Moreover, the analysis on the real datasets demonstrated the effectiveness of TNFE for providing early-warning signals on various diseases. In addition, we also predicted disease deterioration risk and identified drug targets for cancers based on stage-wise data.<\/jats:p>","DOI":"10.1093\/bib\/bbac164","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:16:25Z","timestamp":1649934985000},"source":"Crossref","is-referenced-by-count":21,"title":["Detecting the critical states during disease development based on temporal network flow entropy"],"prefix":"10.1093","volume":"23","author":[{"given":"Rong","family":"Gao","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Henan University of Science and Technology , Luoyang 471023, China"}]},{"given":"Jinling","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Henan University of Science and Technology , Luoyang 471023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5545-6185","authenticated-orcid":false,"given":"Peiluan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Henan University of Science and Technology , Luoyang 471023, China"}]},{"given":"Luonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai 200031, China"},{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences , Hangzhou 310024, China"},{"name":"Guangdong Institute of Intelligence Science and Technology , Hengqin, Zhuhai, Guangdong 519031, China"},{"name":"International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo , Tokyo 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