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

Overview

1. Department of Oral Pathology, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iaşi, Romania
2. Department of Oncology and Radiotherapy, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iaşi, Romania
3. Department of Clinical Laboratory, “Sfântul Spiridon” Emergency Hospital, 700111 Iași, Romania
4. National Institute of Research and Development for Technical Physics—IFT Iași, 700050 Iași, Romania
5. Clinical Emergency Hospital “Prof. Dr. Nicolae Oblu” Iași, 700309 Iași, Romania
6. Department of Environmental Engineering, Mechanical Engineering and Agritourism, Faculty of Engineering, “Vasile Alecsandri” University of Bacău, 600115 Bacău, Romania; (F.N.); (V.N.); (M.P.-L.); (C.M.T.)
7. Faculty of Physics, Alexandru Ioan Cuza University of Iași, 700506 Iași, Romania
8. Department of Radiotherapy, Regional Institute of Oncology, 700483 Iași, Romania
9. Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy Iași, 700115 Iași, Romania; (L.O.); (D.V.)

Abstract

Complex diseases often involve distributed interactions among biological regions, physiological systems, imaging phenotypes, and clinical variables that are not fully captured by anatomical proximity, isolated biomarkers, or conventional feature-based representations. In oncology, neuroimaging, critical care, and systems medicine, distant or apparently separate biomedical sectors may show strong statistical or functional coupling associated with multimodal imaging signatures, inflammatory responses, metabolic constraints, treatment-induced changes, or shared disease-state organization. In this work, we introduce a proof-of-concept relational graph framework for representing such candidate hidden connectivity in terms of correlation-induced accessibility bridges. The novelty of the framework is that it does not treat biomedical correlation, graph distance, and network connectivity as separate descriptors but explicitly couples non-factorizable inter-sector correlation to localized accessibility compression in an emergent disease-state geometry. The proposed framework represents a biomedical system as a weighted relational graph in which nodes correspond to clinically relevant entities, such as tissue regions, imaging-derived features, biomarker modules, physiological variables, or disease states, while weighted edges encode constraints on functional, statistical, or pathological accessibility. Within this structure, coarse-grained biomedical sectors are defined as organized subsystems, and non-factorizable coupling between sectors is quantified using mutual-information-type measures. Candidate biomedical bridges are then defined operationally as localized, high-gain reductions in effective inter-sector accessibility distance. We introduce explicit coupling rules linking sector-level correlation to bridge-specific accessibility compression, including an effective distance-compression model and an ensemble-based formulation. Numerical proof-of-concept simulations on randomized modular graph ensembles show that increasing correlation strength systematically reduces effective inter-sector distance and increases bridge gain. The strongest compression occurs when correlation modulates a designated bridge architecture, exceeding the effects observed under random non-bridge or generic inter-sector modulation. These simulations are not intended to validate a disease-specific biological mechanism but to test whether the proposed correlation–compression rule produces bridge-specific effects distinguishable from null graph perturbations. The resulting structures should not be interpreted as physical anatomical tunnels or direct causal pathways unless supported by additional biological evidence. Rather, they represent correlation-induced accessibility bridges: localized, high-gain routes in a patient- or disease-specific relational geometry. The framework may therefore provide a theoretical and computational basis for prioritizing candidate hidden connectivity patterns in radiomics, multimodal prognosis, physiological deterioration, recurrence modeling, and systems-level disease networks.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

No new experimental or clinical data were generated or analyzed in this study. The illustrative numerical simulations used to generate the conceptual figures are based on phenomenological equations described in the manuscript, and the corresponding code can be made available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 12 keywords, 25 references.

Cite

This paper

Iancu, R. I., Buzea, C. G., Nedeff, F., Mirilă, D., Nedeff, V., Panainte-Lehaduș, M., Tomozei, C. M., Agop, M., Doboș, A. Ș., Iancu, D. P. T., Ochiuz, L., & Vasincu, D. (2026). Correlation-Induced Accessibility Bridges in Biomedical Networks: A Proof-of-Concept Relational Graph Model. Entropy (Basel, Switzerland), 28(7), 769. https://doi.org/10.3390/e28070769

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3389/fonc.2026.1816015 [code]

Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases.

Journal: Frontiers in oncology

In common: other condition, 3 references

[2] doi:10.1186/s13244-026-02296-3 [code]

A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.

Journal: Insights into imaging

In common: other condition, 2 references

[3] doi:10.1038/s41531-026-01408-6 [code]

A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.

Journal: NPJ Parkinson's disease

In common: clinical / translational, 2 references

[4] doi:10.1016/j.isci.2026.116121 [code]

Multi-omics reveal phosphatidic acid phosphatases modify Niemann-Pick type C disease severity.

Journal: iScience

In common: 2 references

[5] doi:10.1016/j.patter.2026.101619 [code]

Sampling bias corrections for discrete and Gaussian partial information decompositions.

Journal: Patterns (New York, N.Y.)

In common: 2 references

[6] doi:10.1371/journal.pone.0348608

A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.

Journal: PloS one

In common: 2 references

[7] doi:10.1371/journal.pone.0344600 [code]

Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.

Journal: PloS one

In common: clinical / translational, other condition, 1 reference

[8] doi:10.1186/s12880-026-02434-9 [code]

Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models.

Journal: BMC medical imaging

In common: clinical / translational, other condition, 1 reference

[9] doi:10.1038/s41598-026-59052-2

A machine learning framework for multimodal temporal prediction of neurological outcome after out-of-hospital cardiac arrest.

Journal: Scientific reports

In common: clinical / translational, 1 reference

[10] doi:10.1080/0886022x.2026.2732422

Serum BDNF and cognitive risk in maintenance hemodialysis: a machine learning study.

Journal: Renal failure

In common: clinical / translational, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.