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This study introduces individual-specific edge-network analysis (iENA) to predict pre-disease states from single omics samples. iENA accurately detects early warning signals for infections and identifies critical cancer stages using individual-specific biomarkers.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biomedical Informatics

Background:

  • Predicting pre-disease states is challenging, especially with limited clinical data.
  • Traditional Edge-Network Analysis (ENA) requires multiple samples per individual, hindering personalized medicine.
  • Dynamic Network Biomarker (DNB) theory offers a framework for analyzing complex biological networks.

Purpose of the Study:

  • To develop an individual-specific ENA (iENA) method for single-sample pre-disease state prediction.
  • To identify individual-specific biomarkers for early disease detection and diagnosis.
  • To overcome the limitations of traditional ENA in personalized medicine.

Main Methods:

  • Proposed iENA, integrating DNB theory for analyzing high-dimensional omics data.
  • Applied iENA to H3N2 influenza infection omics data for individual prediction.
  • Utilized iENA for critical stage detection in multiple cancers using omics data.

Main Results:

  • iENA accurately detected early warning signals for influenza infection in individuals (AUC > 0.9).
  • Identified novel individual-specific biomarkers and recovered known influenza biomarkers.
  • Detected critical cancer stages with significant edge-biomarkers, validated by survival analysis.

Conclusions:

  • iENA enables accurate, single-sample prediction of pre-disease states and individual-specific biomarkers.
  • This approach advances personalized medicine by enabling early disease detection.
  • iENA shows promise for identifying critical disease stages across various conditions.