Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis
Joint Authors
Zou, Xiufen
Wang, Gang
Li, Yuanyuan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-01
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system.
Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules.
Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson’s correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases.
To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state.
The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.
American Psychological Association (APA)
Wang, Gang& Li, Yuanyuan& Zou, Xiufen. 2017. Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142305
Modern Language Association (MLA)
Wang, Gang…[et al.]. Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142305
American Medical Association (AMA)
Wang, Gang& Li, Yuanyuan& Zou, Xiufen. Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142305
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142305