"Variants are unscored" was accurate: nothing was ever trained, so a quarter of every rank was
dead weight and the UI leaked a connection error at the reader.
- scripts/make-training-set.sh derives a training table from ClinVar directly. ClinVar already
carries the molecular consequence, the gene and an allele frequency, which is the feature set
serving sends, so this avoids running VEP over hundreds of thousands of variants. 2-star
records only.
- train.py now holds out whole genes (GroupShuffleSplit). docs/data.md had said to do this since
the data pass; the code was still doing a random split, which is the leak Grimm 2015 describes.
- evaluate() reports missense on its own. On the last run: AUROC 0.986 over 74,239 held-out
variants, but 0.872 over the 13,553 missense ones, and the docs say plainly why even that is
flattered — within missense the only live feature is allele frequency, and ClinVar's benign
calls often use allele frequency as evidence (ACMG BA1/BS1), so the feature partly caused the
label.
- the 503 now names what is missing (model@alias via tracking URI) and leaves the exception in
the server log instead of the UI.
- make training-set / make train; the 58 MB table is gitignored.
Verified end to end: model registered as v2, the simulated NF2 case scores 0.999 on the planted
variant, and it now ranks 1.00 with all four components live.
Tests: api 77, ml 22, loader 16, web 32; ruff, mypy, svelte-check clean.
Kemal Yaylali
197975cc42