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Detecting the critical states during disease development based on temporal network flow entropy.

Rong Gao1, Jinling Yan1, Peiluan Li1

  • 1School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China.

Briefings in Bioinformatics
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Summary

This study introduces a novel method, temporal network flow entropy (TNFE), to detect critical states in complex diseases using molecular network fluctuations. TNFE successfully identifies early warning signals for disease deterioration, outperforming existing methods.

Keywords:
critical statedynamic network biomarker (DNB)high dimensional omics datanetwork flow entropy (NFE)

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Area of Science:

  • Biomedical data analysis
  • Systems biology
  • Network medicine

Background:

  • Complex diseases progress through normal, critical, and disease states, with sudden deterioration often linked to critical transitions.
  • Detecting these critical states, especially in high-dimensional data at an individual level, remains a significant challenge.
  • Early identification of tipping points is crucial for preventing disease progression.

Purpose of the Study:

  • To develop a novel method for detecting critical states of complex diseases in individuals using high-dimensional omics data.
  • To identify dynamic network biomarkers associated with critical states.
  • To provide early-warning signals for disease deterioration and predict risks.

Main Methods:

  • Development of temporal network flow entropy (TNFE) based on molecular network fluctuations.
  • Application of TNFE to simulated and real-world high-dimensional omics datasets (time-course and stage-course).
  • Evaluation of TNFE's robustness, superiority over existing methods, and effectiveness in real disease analysis.

Main Results:

  • Successful detection of critical states preceding disease deterioration in simulated and real datasets (respiratory viral infections, tumors).
  • Identification of dynamic network biomarkers for critical states.
  • Demonstrated robustness of TNFE to noise and its superiority compared to existing methods.
  • Validation of TNFE's effectiveness in providing early-warning signals for diverse diseases.

Conclusions:

  • TNFE is a robust and effective method for detecting critical states in complex diseases at an individual level.
  • The method provides valuable early-warning signals, enabling timely intervention and risk prediction.
  • TNFE facilitates the identification of potential therapeutic targets, such as in cancer treatment.