Detecting the tipping points in a three-state model of complex diseases by temporal differential networks

@article{Chen2017DetectingTT,
  title={Detecting the tipping points in a three-state model of complex diseases by temporal differential networks},
  author={Pei Chen and Yongjun Li and Xiaoping Liu and Rui Liu and Luonan Chen},
  journal={Journal of Translational Medicine},
  year={2017},
  volume={15}
}
BackgroundThe progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a critical state or tipping point immediately preceding the disease state. Traditional biomarkers aim to identify a disease state by exploiting the information of differential expressions for the observed molecules, but may fail to detect a pre-disease state because there are… Expand

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References

SHOWING 1-10 OF 36 REFERENCES
Identifying critical transitions of complex diseases based on a single sample
TLDR
A novel computational approach based on the DNB theory and differential distributions between the expressions of DNB and non-DNB molecules is developed, which can detect the pre-disease state reliably even from a single sample taken from one individual, by compensating insufficient samples with existing datasets from population studies. Expand
Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers
TLDR
This study detected early-warning signals of T1D and its leading biomolecular networks based on serial gene expression profiles of NOD (non-obese diabetic) mice by identifying a new type of biomarker, i.e., dynamical network biomarker (DNB) which forms a specific module for marking the time period just before the drastic deterioration of T2D. Expand
The decrease of consistence probability: at the crossroad of catastrophic transition of a biological system
TLDR
This work presents a hidden-Markov-model (HMM) based computational method to identify the pre-disease state and elucidate the essential mechanisms during the critical transition at the network level and proposes a consistence score to measure the probability that a system is in consistency with the normal state. Expand
Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers
TLDR
A model-free method to detect early-warning signals of critical transitions of complex diseases, even with only a small number of samples is developed, and it is shown that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. Expand
Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis
TLDR
DNB can not only signal the emergence of the critical transitions for early diagnosis of diseases, but can also provide the causal network of the transitions for revealing molecular mechanisms of disease initiation and progression at a network level. Expand
Identifying critical transitions and their leading biomolecular networks in complex diseases
TLDR
A state-transition-based local network entropy (SNE) is defined and it is proved that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. Expand
Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers
TLDR
The new concept of dynamical network biomarkers (DNBs) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve “real” early diagnosis of complex diseases. Expand
Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers
TLDR
This work applies the dynamical network biomarker (DNB) to detect an early-warning signal of breast cancer on the basis of gene expression data of MCF-7 cell differentiation and finds a number of the related modules and pathways in the samples, which can be used not only as the biomarkers of cancer cells but also as the drug targets. Expand
Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes
TLDR
Recent advances on dynamical network biomarkers (DNBs) as well as the related theoretical foundation are reviewed, which can identify not only early signals of the critical transitions but also their leading networks, which drive the whole system to initiate such transitions. Expand
Identifying disease genes and module biomarkers by differential interactions
TLDR
A novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which can be used as robust module biomarkers. Expand
...
1
2
3
4
...