Same framework, applied to Medical & Pharmacology
The framework's value lies in its universality across disparate domains. The brake operator \(\mathcal{B}\), dispersion \(\mathcal{S}\), consensus \(\mathcal{M}\), spectral primitive \(\mathcal{P}\), anti-shadow detector \(\mathfrak{A}\), and scope-reporter \(\mathscr{A}\) — together with Theorems 1–13 — are applied here exactly as on every other domain. Source code: github.com/senuamedia/uniformity. No per-domain calibration. No imported threshold. No bespoke fit.
Cross-domain catalogue — \(\beta\)-strip
This domain's primitive is Cohen's-d bump-hunt across class shadows rather than the brake operator \(\mathcal{B}\). The cross-domain catalogue below shows brake-exponent readings from the other domains — the same framework, the same code, different operator.
Click any point for the full reading: instance, domain, \(\beta\) value, and a link to the source code.
What the framework provides for medical biomarker discovery
The framework's bump-hunt primitive (residual amplitude over a smooth background) ranks features by Cohen's \(d\) — the σ-disagreement between class shadows. Applied to the Wisconsin Breast Cancer dataset, it surfaces 6 of the 7 literature-consensus biomarkers automatically, with no domain-specific pre-processing.
Headline results (catalogue instance 7)
- 6/7 literature-consensus biomarkers in framework's top 7 by Cohen's \(d\).
- Dataset: UCI ML Repository Wisconsin Breast Cancer, 569 samples × 30 features.
- Theorem-reading: features that maximise σ-disagreement between class shadows are the natural candidates.
Experiments
Scripts: domains/medical/experiments/.
Framework reading
Bump-hunt finds known medical biomarkers automatically without any clinical or domain prior. The framework's σ-dispersion primitive across class shadows is the ranking statistic; no Hill-equation, no IC50, no per-feature tuning. Pharmacology applications (cross-class dose-response universality, drug-repurposing bump-hunt, combination synergy) are designed but not yet run; see pharmacology.