Audit the declarations a computational result carries — and check they are still true. Producer freshness, counter coherence, provenance pins, partial runs. Read-only by construction.
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Updated
Aug 30, 2026 - Python
Audit the declarations a computational result carries — and check they are still true. Producer freshness, counter coherence, provenance pins, partial runs. Read-only by construction.
Repository for the 3rd paper of my PhD.
Source-available IX-Function causal-transfer harness: tests cross-domain causal structure reuse through pre-outcome prediction, reality-delta scoring, uncertainty preservation, falsification, negative controls, model review, donor handoffs, and bounded Wave 6 review gates. No AGI claim.
R package for negative control analysis.
Causal Inference for Multiomics
The same design filter scores AUC 0.663 against naive controls and 0.547 (CI spanning 0.5) against composition-preserving scrambles. The null is the result.
Make research claims earn their way out of a pipeline: claims registry, fail-closed release gates, blind locks, negative controls
De novo cosmetic peptide design from the skin ECM degradome, scored against composition-preserving nulls
A tiered reference set of 797 candidate non-interacting drug pairs for benchmarking drug–drug interaction prediction, with FAERS co-exposure denominators, statistical-power tiers, and a reproducibility harness.
Small checks that prove they can fail. A guardrail nobody has seen fire is theater — so every tool ships negative controls and a --selftest that runs them.
Property-matched decoys cut a DHFR screen's property-only AUC from 0.913 to 0.841 - not to 0.5. Matching reduces decoy bias without removing it.
Evidence-centric AI governance tools: PASS / ACT / CT-safe evaluation harness, specs, and examples (public subset).
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