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The decrease of consistence probability: at the crossroad of catastrophic transition of a biological system.

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Summary

Identifying pre-disease states in complex diseases is challenging. This study introduces a hidden Markov model (HMM) to detect early disease transitions, successfully applied to cancer and viral infections.

Keywords:
Dynamical network biomarkerHidden Markov processPre-disease states

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Medicine

Background:

  • Detecting pre-disease states in complex diseases is challenging due to biological system complexity and subtle early changes.
  • Traditional disease detection methods often miss the critical transition phase before serious deterioration.

Purpose of the Study:

  • To develop a computational method for identifying pre-disease states and understanding critical transition mechanisms.
  • To exploit dynamical differences between normal and pre-disease states for early detection.

Main Methods:

  • Utilized a hidden Markov model (HMM) to analyze network dynamics.
  • Developed a consistency score to quantify system deviation from the normal state.
  • Considered network variation and stationary Markov processes to model pre-disease states.

Main Results:

  • Successfully identified pre-disease states in simulated networks and real-world high-throughput microarray data.
  • Demonstrated effectiveness in detecting critical transitions for HCV-induced hepatocellular carcinoma.
  • Validated the approach for identifying pre-disease states in virus-induced influenza infection.

Conclusions:

  • Critical transition phenomena in biological processes exhibit generic dynamical properties.
  • The developed HMM-based method can effectively detect these generic properties for early disease identification.