ArticleFundamental research2025
Disease prediction by network information gain on a single sample basis.
Article in Fundamental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- HNPP: Higher-order network-based personalized PageRank for detecting critical phase in complex biological systems.PLoS computational biology · 2026Article
- A Comprehensive Comparison of Transition Point Detection Methods for Monkeypox - Africa, 2024-2025.China CDC weekly · 2026Article
- CDNFE suggests FNDC3B and NECTIN4 as drivers of precancer progression via PI3K/AKT EMT.NPJ precision oncology · 2026Article
- Detection of pre-transition phases during biological development using single-sample network entropy (SNE).NPJ systems biology and applications · 2025Article
- DNFE: Directed network flow entropy for detecting tipping points during biological processes.PLoS computational biology · 2025Article
- Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE).Research (Washington, D.C.) · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
There are critical transition phenomena during the progression of many diseases. Such critical transitions are usually accompanied by catastrophic disease deterioration, and their prediction is of significant importance for disease prevention and treatment. However, predicting disease deterioration solely based on a single sample is a difficult problem. In this study, we presented the network information gain (NIG) method, for predicting the critical transitions or disease state based on network flow entropy from omics data of each individual. NIG can not only efficiently predict disease deteriorations but also detect their dynamic network biomarkers on an individual basis and further identify potential therapeutic targets. The numerical simulation demonstrates the effectiveness of NIG. Moreover, our method was validated by successfully predicting disease deteriorations and identifying their potential therapeutic targets from four real omics datasets, i.e., an influenza dataset and three cancer datasets.
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Registered trials
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