Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers
By Luonan Chen, Rui Liu, Zhiping Liu, Meiyi Li, Kazuyuki Aihara. Scientific Reports, 2012
607 citations recorded by the source corpus.
Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions, even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show 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. We employ microarray data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data and functional analysis.
Subfields: Experimental and Cognitive Psychology, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c7f7d4ddb946d2553241ea19a85f34ef1808403f
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In Experimental and Cognitive Psychology
- Matjaz Perc
- Filippo Radicchi
- H. Eugene Stanley
- Hernán A. Makse
- Jianxi Gao
- Manlio De Domenico
- Mark Newman
- Attila Szolnoki
In Global and Planetary Change
Citations in the corpus
- Critical slowing down as early warning for the onset and termination of depression (2013, unknown): “Besides, in a network of fluctuating elements, one expects an increase in cross-correlation between elements that will shift together (38).”
- Network resilience (2020, methodology): “48, the connections between DNBs become intense and the standard deviations of the DNBs’ states increase, when the gene co-expression network is in the pre-disease state.”
- Robustness and resilience of complex networks (2024, background): “In such cases, early-warning indicators of an impending network collapse 68,209–214 and repairs and adaptive responses to an already evolving collapse 215–217 become the best line of defence.”