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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Resilience in Clinical Care: Getting a Grip on the Recovery Potential of Older Adults
Gijzel, S. M. W., Whitson, H. E., van de Leemput, I. A., Scheffer, M., van Asselt, D., Rector, J. L., Olde Rikkert, M. G. M., & Melis, R. J. F. (2019) · Journal of the American Geriatrics Society · doi:10.1111/jgs.16149 · Cited by 92
Multiscale Analysis of Intensive Longitudinal Biomedical Signals and Its Clinical Applications
Nakamura, T., Kiyono, K., Wendt, H., Abry, P., & Yamamoto, Y. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2491979 · Cited by 64
Biomedical data, computational methods and tools for evaluating disease–disease associations
Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac006 · Cited by 49
Self-citationNetwork control principles for identifying personalized driver genes in cancer
Guo, W.-F., Zhang, S.-W., Zeng, T., Akutsu, T., & Chen, L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz089 · Cited by 44
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
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. https://app.cochat.ai/papers/10.1038/srep00342
- The paper
- Chen, L., Liu, R., Liu, Z.-P., Li, M., & Aihara, K. (2012). Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Scientific Reports. https://doi.org/10.1038/srep00342
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.