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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Identifying critical transitions in complex diseases.

Smita Deb1, Subhendu Bhandary, Sudipta Kumar Sinha

  • 1Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar 140 001, India.

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Early disease detection is crucial for global health. This review explores critical transitions in complex diseases, highlighting resilience theory and molecular biomarkers for forecasting disease onset and improving healthcare.

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Area of Science:

  • Complex systems biology
  • Medical informatics
  • Public health

Background:

  • Global mortality and disease burden are increasing with societal development.
  • Early detection of complex diseases is vital but challenging.
  • Understanding disease onset as critical transitions is key.

Purpose of the Study:

  • To review research on critical transitions in complex diseases.
  • To explore the application of resilience and critical slowing down theory.
  • To discuss molecular mechanisms and dynamic network biomarkers for early warning signals.

Main Methods:

  • Literature review of complex disease research.
  • Analysis of resilience and critical slowing down theories in disease.
  • Examination of molecular mechanisms and network biomarkers.

Main Results:

  • Critical transitions mark the shift from healthy to diseased states.
  • Resilience theory and critical slowing down offer forecasting tools.
  • Molecular mechanisms and dynamic network biomarkers can predict transitions, even without critical slowing down.

Conclusions:

  • Integrating complex disease theory with resilience is essential.
  • Advanced research is needed for developing robust early warning signals.
  • Future prospects lie in harnessing molecular insights for predictive medicine.