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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Disease prediction by network information gain on a single sample basis.

Jinling Yan^1,2, Peiluan Li^1,3, 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.

Keywords:

Disease prediction Drug target Dynamic network biomarker Network flow entropy Network information gain Tipping point

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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.