scholarly journals Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Fatemeh Vafaee
Keyword(s):  
Early Warning ◽  
Complex Diseases ◽  
Multi Objective Optimization ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Multi Objective ◽  
Dynamical Network ◽  
Network Biomarkers

Early-warning signals using dynamical network markers selected by covariance

2019 ◽  
Vol 100 (5) ◽  
Author(s):  
Tadayoshi Matsumori ◽  
Hiroyuki Sakai ◽  
Kazuyuki Aihara
Keyword(s):  
Early Warning ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Dynamical Network

scholarly journals Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Gang Wang ◽  
Yuanyuan Li ◽  
Xiufen Zou
Keyword(s):  
Early Warning ◽  
Self Assembly ◽  
Disease Onset ◽  
Complex Diseases ◽  
Critical Threshold ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Development And Differentiation ◽  
Quantitative Changes

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.

scholarly journals Personalized Early-Warning Signals during Progression of Human Coronary Atherosclerosis by Landscape Dynamic Network Biomarker

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 676
Author(s):  
Jing Ge ◽  
Chenxi Song ◽  
Chengming Zhang ◽  
Xiaoping Liu ◽  
Jingzhou Chen ◽  
...  
Keyword(s):  
Early Warning ◽  
Coronary Atherosclerosis ◽  
Dynamic Network ◽  
Tipping Point ◽  
Warning Signals ◽  
Metabolomics Data ◽  
Early Warning Signals ◽  
Network Biomarkers ◽  
Landscape Dynamic ◽  
Major Factors

Coronary atherosclerosis is one of the major factors causing cardiovascular diseases. However, identifying the tipping point (predisease state of disease) and detecting early-warning signals of human coronary atherosclerosis for individual patients are still great challenges. The landscape dynamic network biomarkers (l-DNB) methodology is based on the theory of dynamic network biomarkers (DNBs), and can use only one-sample omics data to identify the tipping point of complex diseases, such as coronary atherosclerosis. Based on the l-DNB methodology, by using the metabolomics data of plasma of patients with coronary atherosclerosis at different stages, we accurately detected the early-warning signals of each patient. Moreover, we also discovered a group of dynamic network biomarkers (DNBs) which play key roles in driving the progression of the disease. Our study provides a new insight into the individualized early diagnosis of coronary atherosclerosis and may contribute to the development of personalized medicine.

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