Detecting the Pre-Disease State of Single Sample Through the Change in Local Network Enrichment Level

Area of Science:

• Systems Biology
• Computational Biology
• Preventive Medicine

Background:

• Complex biological systems exhibit tipping points leading to disease states.
• Early detection of pre-disease states is vital for preventive medicine.
• The single-sample problem complicates accurate pre-disease state identification.

Purpose of the Study:

• To develop a novel method for single-sample pre-disease state detection.
• To address the challenge of identifying subtle shifts before overt disease manifestation.
• To provide a tool for early intervention in complex biological processes.

Main Methods:

• Introduced a new method based on analyzing changes in local network enrichment levels.
• Applied the method to analyze five independent real-world datasets.
• Validated the approach on influenza virus infection and tumor datasets.

Main Results:

• The method accurately identified pre-disease states across diverse datasets before clinical onset.
• Key genes identified were linked to viral infection, immune dysregulation, and tumor metastasis.
• Demonstrated robustness and biological interpretability of the findings.

Conclusions:

• The developed method is a reliable tool for single-sample pre-disease state detection.
• The approach offers biological insights into disease mechanisms.
• Significant potential for clinical translation in personalized preventive medicine.