ArticlePLoS computational biology2017
Quantifying critical states of complex diseases using single-sample dynamic network biomarkers.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Detecting the Pre-Disease State of Single Sample Through the Change in Local Network Enrichment Level.Genes · 2026Article
- Characterising Epigenetic Tipping Points using a Spectral Dimension Reduction Approach.Bulletin of mathematical biology · 2026Article
- Network-level organization of systemic inflammation reflects early Alzheimer's-like behavioral changes.Scientific reports · 2025Article
- A Multidimensional Definition of Pre-Osteoarthritis: Toward 21st-Century Subclinical Detection and Targeted Intervention.International journal of molecular sciences · 2025Review
- Network-Level Organization of Systemic Inflammation Reflects Early Alzheimer's-Like Behavioral Changes.Research square · 2025Article
- Dynamic network entropy for pinpointing the pre-outbreak stage of infectious disease.Journal of the Royal Society, Interface · 2025Article
- Genomic-Thermodynamic Phase Synchronization: Maxwell's Demon-like Regulation of Cell Fate Transition.International journal of molecular sciences · 2025Article
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- Identification of Critical States in Complex Biological Systems Using Cell-Specific Causal Network Entropy.Research (Washington, D.C.) · 2025Article
- Disease prediction by network information gain on a single sample basis.Fundamental research · 2025Article
- Detecting tipping points of complex diseases by network information entropy.Briefings in bioinformatics · 2024Article
- Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool.Biomolecules · 2023Review
- Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning.Computational and structural biotechnology journal · 2023Article
- Dynamics of a model for the degradation mechanism of aggregated α-synuclein in Parkinson's disease.Frontiers in computational neuroscience · 2023Article
- The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression.Journal of molecular cell biology · 2022Article
- Identifying the critical state of complex biological systems by the directed-network rank score method.Bioinformatics (Oxford, England) · 2022Article
- Artificial neural network applied to fragile X-associated tremor/ataxia syndrome stage diagnosis based on peripheral mitochondrial bioenergetics and brain imaging outcomes.Scientific reports · 2022Article
- Molecular biomarkers, network biomarkers, and dynamic network biomarkers for diagnosis and prediction of rare diseases.Fundamental research · 2022Review
- Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors.Nucleic acids research · 2022Article
- Identifying network biomarkers of cancer by sample-specific differential network.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to "diagnose disease", sDNB is based on the information of differential associations, thereby having the ability to "predict disease" or "diagnose near-future disease". Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.
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