ArticleFrontiers in genetics2019
Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Detection of critical transition states in complex diseases based on distance correlation coefficient.PloS one · 2026Article
- DNFE: Directed network flow entropy for detecting tipping points during biological processes.PLoS computational biology · 2025Article
- Identifying critical States of complex diseases by local network Wasserstein distance.Scientific reports · 2025Article
- Revealing the critical state and identifying individualized dynamic network biomarker for type 2 diabetes through advanced analysis methods on individual basis.Scientific reports · 2025Article
- Disease prediction by network information gain on a single sample basis.Fundamental research · 2025Article
- CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes.BMC bioinformatics · 2024Article
- Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE).Research (Washington, D.C.) · 2024Article
- Dynamics of a model for the degradation mechanism of aggregated α-synuclein in Parkinson's disease.Frontiers in computational neuroscience · 2023Article
- Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.Computational and structural biotechnology journal · 2022Review
- Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine.Current opinion in systems biology · 2020Article
- Identifying critical state of complex diseases by single-sample Kullback-Leibler divergence.BMC genomics · 2020Article
- Single-Sample Node Entropy for Molecular Transition in Pre-deterioration Stage of Cancer.Frontiers in bioengineering and biotechnology · 2020Article
- mNFE: microbiome network flow entropy for detecting pre-disease states of type 1 diabetes.Gut microbesArticle
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5 authors.
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Abstract
The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex diseases. However, with only a case sample, it is challenging to detect a pre-disease state which has little significant differences comparing with a normal state in terms of phenotypes and gene expressions. In this study, by regarding the tipping point as the end point of a stationary Markov process, we proposed a single-sample-based hidden Markov model (HMM) approach to explore the dynamical differences between a normal and a pre-disease states, and thus can signal the upcoming critical transition immediately after a pre-disease state. Using this method, we identified the pre-disease state or tipping point in a numerical simulation and two real datasets including stomach adenocarcinoma and influenza infection, which demonstrate the effectiveness of the method.
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