The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers
Open accessAbstract
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.
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Cites
Early-warning signals for critical transitions
Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., & Sugihara, G. (2009) · Nature · doi:10.1038/nature08227 · Cited by 4,854
Tipping elements in the Earth’s climate system
Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705414105 · Cited by 3,982
May, R. M., Levin, S. A., & Sugihara, G. (2008) · Nature · doi:10.1038/451893a · Cited by 797
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Biomedical data, computational methods and tools for evaluating disease–disease associations
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Self-citationNetwork control principles for identifying personalized driver genes in cancer
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Self-citationLiang, J., Li, Z.-W., Yue, C.-T., Hu, Z., Cheng, H., Liu, Z.-X., & Guo, W.-F. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac254 · Cited by 23
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Self-citationUsing Physiological System Networks to Elaborate Resilience Across Frailty States
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Metadata
- Authors
- Chen, L., Liu, R., Liu, Z.-P., Li, M., & Aihara, K.
- Venue
- Scientific Reports
- Published
- 2012-03-29
- Volume
- 2
- Issue
- 1
- Pages
- 342-342
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2035471593
- Cited by
- 654
- References
- 3
- FWCI
- 13.36
- Type
- journal article
- Language
- en
- Open access
- gold
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