Correlation-Induced Accessibility Bridges in Biomedical Networks: A Proof-of-Concept Relational Graph Model

Area of Science:

• Systems Medicine and Bioinformatics
• Network Science in Biology
• Computational Pathology

Background:

• Complex diseases involve interactions beyond anatomical proximity.
• Conventional methods struggle to capture distributed biological interactions.
• Hidden connectivity is crucial for understanding disease mechanisms.

Purpose of the Study:

• To develop a relational graph framework for representing hidden connectivity in complex diseases.
• To couple inter-sector correlation with accessibility compression in a disease-state geometry.
• To operationalize candidate biomedical bridges as correlation-induced accessibility routes.

Main Methods:

• Representing biomedical systems as weighted relational graphs with nodes as clinical entities and edges as accessibility constraints.
• Quantifying non-factorizable coupling between coarse-grained biomedical sectors using mutual-information-type measures.
• Defining biomedical bridges as localized, high-gain reductions in effective inter-sector accessibility distance, coupled via explicit rules.

Main Results:

• Simulations show increasing correlation strength systematically reduces effective inter-sector distance and increases bridge gain.
• The strongest compression occurred when correlation modulated a designated bridge architecture.
• The framework successfully distinguished bridge-specific effects from null graph perturbations.

Conclusions:

• The proposed framework provides a theoretical and computational basis for identifying hidden connectivity in complex diseases.
• Correlation-induced accessibility bridges represent patient- or disease-specific relational geometries.
• This approach can prioritize candidate hidden connectivity patterns in various biomedical applications like radiomics and systems-level disease networks.