| # Structural Disease Networks — TCGE Benchmark |
|
|
| A curated collection of disease signaling networks analyzed with |
| the Theory of Emergent Global Constraints (TCGE) framework. |
|
|
| ## Overview |
| - **Networks**: 100+ disease signaling networks |
| - **Domains**: Oncology, Pneumology, Neurology, Immunology |
| - **Metrics**: Fragility Index (FI), algebraic connectivity (λ₂), |
| spectral entropy (EPS_z), edge criticality, bridge analysis |
| - **Drugs mapped**: 50+ approved therapies with structural profiles |
| |
| ## Key Finding |
| The Fragility Index predicts therapeutic response class: |
| - FI > 90%: Ultra-fragile → treatment-refractory (COPD) |
| - FI ≥ 80%: Hyper-fragile → structural restoration transforms outcome (CF/Trikafta) |
| - FI > 60%: Fragile → monotherapy works (asthma, IPF) |
| - FI ≈ 50%: Resilient → combination required (NSCLC) |
| |
| ## Platform |
| Full analysis engine: https://cognitive-engineering.dev |
| |
| ## Citation |
| |
| If you use this dataset, please cite: |
| |
| @article{venti2026structural, |
| title={Protein regulatory architectures segregate into two topological |
| regimes: distributed cooperative systems and fragile signalling cascades}, |
| author={Venti, David Martin}, |
| year={2026}, |
| doi={10.5281/zenodo.19045028} |
| } |
| |
| @article{venti2026triangular, |
| title={Triangular solidification: local motif redundancy drives a sharp |
| structural transition in complex networks}, |
| author={Venti, David Martin}, |
| year={2026}, |
| doi={10.5281/zenodo.19163066} |
| } |
| |
| ## License |
| CC BY 4.0 |
