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Updated: May 16, 2025
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
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Disease prediction by network information gain on a single sample basis.
Jinling Yan1,2, Peiluan Li1,3, Ying Li1
1School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China.
Fundamental Research
|April 1, 2025
Summary
Predicting disease deterioration is crucial for patient outcomes. This study introduces the network information gain (NIG) method to forecast critical transitions using omics data, identifying biomarkers and therapeutic targets.
Area of Science:
- Computational biology
- Systems biology
- Biomedical data science
Background:
- Critical transitions in disease progression often lead to severe deterioration.
- Predicting these transitions from single-sample omics data is challenging.
- Early prediction is vital for effective disease prevention and treatment.
Purpose of the Study:
- To develop a novel method for predicting critical disease transitions and deterioration.
- To identify dynamic network biomarkers and potential therapeutic targets on an individual basis.
- To leverage omics data and network flow entropy for predictive modeling.
Main Methods:
- Introduced the network information gain (NIG) method.
- Utilized network flow entropy from individual omics data.
- Performed numerical simulations to demonstrate NIG's effectiveness.
Main Results:
- NIG successfully predicted critical transitions and disease deterioration.
- The method identified dynamic network biomarkers for individuals.
- Potential therapeutic targets were pinpointed.
- Validated on influenza and three cancer omics datasets.
Conclusions:
- The NIG method offers an effective approach for predicting individual disease deterioration.
- NIG facilitates the discovery of personalized biomarkers and therapeutic strategies.
- This approach holds promise for improving disease management and treatment outcomes.
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