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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Related Experiment Video

Updated: Nov 19, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Physiological Network From Anthropometric and Blood Test Biomarkers.

Antonio Barajas-Martínez1,2, Elizabeth Ibarra-Coronado2,3, Martha Patricia Sierra-Vargas4,5

  • 1Posgrado en Ciencias Biomédicas, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.

Frontiers in Physiology
|January 29, 2021
PubMed
Summary

Complex Inference Networks reveal robust physiological networks from biomarkers. This systems biology approach quantifies homeostasis, offering insights into health and disease.

Keywords:
anthropometric measuresblood test biomarkerscomplex inference networkhomeostasisphysiological networks

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Area of Science:

  • Physiology
  • Systems Biology
  • Network Science

Background:

  • Current physiological research often employs reductionist strategies, focusing on molecular mechanisms.
  • Understanding the integrated functioning of physiological variables at the organism level remains challenging.
  • Interactions between diverse physiological components are crucial for a holistic systems view.

Purpose of the Study:

  • To develop a systems biology approach for representing physiology as an integrated network.
  • To build robust physiological networks from biomarker data using Complex Inference Networks.
  • To identify network features representative of physiological health.

Main Methods:

  • Utilized two independent databases to generate Spearman correlation matrices for 81 and 54 physiological variables.
  • Constructed physiological networks by applying a p-value threshold to identify statistically significant correlations.
  • Employed unsupervised community detection algorithms to identify functional clusters within the networks.

Main Results:

  • Network topology was sensitive to the p-value threshold, but an optimal threshold was identified using stability and connectedness criteria.
  • Functional clusters identified through community detection correlated well with established medical knowledge.
  • Physiological networks exhibited a topology between random and ordered structures, suggesting robustness and adaptability.

Conclusions:

  • Complex Inference Networks provide a robust and visually understandable systems biology framework for physiological data.
  • Physiological networks enable the quantification of concepts like homeostasis, aiding in health and disease determination.
  • This approach facilitates the exploration of modular functional clusters within physiological systems.