Detecting early-warning signals for influenza A pandemic based on protein dynamical network biomarkers

Aims/Introduction. Evidences have shown that the deteriorated procession of disease is not a smooth change with time and conditions, in which a critical transition point denoted as predisease state drives the state from normal to disease. Considering individual differences, this paper provides a sample-specific method that constructs an index with individual-specific dynamical network biomarkers (DNB) which are defined as early warning index (EWI) for detecting predisease state of individual sample. Based on microarray data of influenza A disease, 144 genes are selected as DNB and the 7th time period is defined as predisease state. In addition, according to functional analysis of the discovered DNB, it is relevant with experience data, which can illustrate the effectiveness of our sample-specific method.

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Identifying early-warning signals of critical transitions with strong noise by dynamical network markers

Scientific Reports ◽

10.1038/srep17501 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 50

Author(s):

Rui Liu ◽

Pei Chen ◽

Kazuyuki Aihara ◽

Luonan Chen

Keyword(s):

Early Warning ◽

Warning Signals ◽

Early Warning Signals ◽

Dynamical Network ◽

Critical Transitions

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

Genes ◽

10.3390/genes11060676 ◽

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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Early warning signals of regime shifts for aquatic systems: Can experiments help to bridge the gap between theory and real-world application?

Ecological Complexity ◽

10.1016/j.ecocom.2021.100944 ◽

2021 ◽

Vol 47 ◽

pp. 100944

Author(s):

Julio Alberto Alegre Stelzer ◽

Jorrit Padric Mesman ◽

Rita Adrian ◽

Bastiaan Willem Ibelings

Keyword(s):

Early Warning ◽

Real World ◽

Regime Shifts ◽

Aquatic Systems ◽

Warning Signals ◽

Early Warning Signals ◽

Real World Application

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Efficacy of early warning signals and spectral periodicity for predicting transitions in bipolar patients: An actigraphy study

Translational Psychiatry ◽

10.1038/s41398-021-01465-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yoram K. Kunkels ◽

Harriëtte Riese ◽

Stefan E. Knapen ◽

Rixt F. Riemersma - van der Lek ◽

Sandip V. George ◽

...

Keyword(s):

Spectral Analysis ◽

Early Warning ◽

Type I ◽

Warning Signals ◽

Early Warning Signals ◽

Research Fields ◽

Mood Episode ◽

Final Sample ◽

Bipolar Patients ◽

Direction Opposite

AbstractEarly-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets.

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