6 Commits

Include renames

Author	SHA1	Message	Date
Kemal Yaylali	e76ae847a1	fix(science): stop scoring evidence that was never looked up ... A review of the ranking's arithmetic found four things wrong, all of which made the score look better informed than it was. Measurements below are from this repo, not estimates. **Components now abstain instead of inventing a number.** A run without a VEP cache returns no allele frequencies, and rarity_score(None) read that as "absent from gnomAD, therefore maximally rare" and awarded every variant a free 0.25. jobs.has_frequencies / has_effect_scores record what the run actually produced, absent components are dropped from the weighted mean, and the remaining weights are renormalised so the score keeps its meaning. The UI shows "not looked up" rather than a bar, and the funnel stops calling a step "rare" when nothing was filtered. **Allele frequency is no longer a model feature.** It dominated: the same missense variant scored 0.887 at AF 0 and 0.0003 at AF 0.01. That double- counted, because the ranking already scores frequency explicitly, putting ~45% of every rank on one measurement; and it was circular, because ACMG assigns ClinVar's benign labels using frequency (BA1/BS1). Retraining without it moves missense AUROC from 0.872 to 0.500 — exactly random. The old figure was allele frequency, not variant-effect knowledge. The model therefore abstains unless CADD or AlphaMissense is present, since otherwise it only restates the consequence class. **Phenotype matching is weighted by information content** and HPO annotations are propagated up the ontology. Counting terms alike let "global developmental delay" (IC 0.93) count as much as "dilated left subclavian artery" (IC 7.88). **A real bug in the propagation, found by checking it.** The ancestor walk read a pre-order DFS backwards, which on a DAG lets a term resolve before one of its parents and inherit that parent alone instead of its lineage. It dropped 399 terms out of the phenotype branch, Camptodactyly and Chiari malformation among them. Now a true post-order, tested against a reference transitive closure. The ontology arithmetic moved to rarelens_ml.hpo so it is covered by tests, and rarelens_ml.benchmark measures the whole thing: across 10,178 published cases the causal gene ranks first 45.9-81.0% of the time against 5,269 genes, versus 0.02% for chance. docs/data.md reports that with its contamination (HPO's annotations come from these same case reports), and includes the measurement showing information-content weighting earns its place while propagation does not - kept anyway, for a reason the docs argue rather than assume.	2026-09-12 11:32:46 +01:00
Kemal Yaylali	197975cc42	feat(ml): train a real model, and report the number that matters rather than the flattering one ... "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.	2026-09-12 09:13:54 +01:00
Kemal Yaylali	3ab404ebe5	fix(web): stop presenting an unscored case as a failed analysis, and four smaller things ... From clicking through the redesigned UI: - scoring a case without a model registry painted a red failure across a case that had in fact analysed fine. It is now a quiet note saying the model term contributes 0, because scoring is an optional fourth of the rank, not the analysis. - the MLflow default moves to port 5001. On macOS, AirPlay Receiver owns 5000, which is why the registry answered "403" rather than refusing the connection; docker-compose publishes 5001 to match. - a funnel step that kept nothing drew a visible bar. Zero now draws zero. - "1 candidates". - the funnel's fixed grid columns forced a horizontal scrollbar on the report. The report also lists the top undecided candidates now: the first thing anyone opens has no decisions in it, and "Shortlisted (0)" alone said nothing about what the tool found. Tests: api 77, web 32; ruff, mypy, svelte-check clean.	2026-09-12 08:49:00 +01:00
Kemal Yaylali	07a01715fd	feat: redesign around phenotype-driven triage, not variant filtering ... A table with filters made the user do the work. Rare disease triage is a different task: which few variants could explain *this* patient's phenotype, and why. The app now answers that, and lets a reviewer act on the answer. Domain - a case is a proband: a VCF plus the HPO terms observed in the patient (samples -> cases) - HPO's gene-to-phenotype annotations are loaded as reference data (scripts/load-hpo.py) - each candidate can be shortlisted or dismissed with a reason and a note Ranking (app/services/triage.py, 21 tests) - weighted sum of phenotype match, rarity, consequence severity and the model's score, with every component shown next to the candidate - rarity and consequence filter; phenotype only ranks, because a real diagnosis can sit in a gene nobody has annotated yet and filtering on it would hide exactly that case - ClinVar is deliberately not an input: it appears beside the result as independent confirmation, so nothing ranks highly merely because ClinVar already said pathogenic UI - the funnel is the headline: variants called -> rare -> coding candidates -> phenotype-matched - ranked candidates with evidence chips, not a grid of everything; filters are demoted - a variant panel showing the score breakdown, the matched HPO terms, the raw VEP record and links out to Ensembl/gnomAD/ClinVar, with the decision controls - a printable case report: phenotype, funnel, shortlisted variants with reasons, provenance API: /cases with phenotypes, /cases/{id}/candidates (funnel + ranked + weights), /variants/{id}, /variants/{id}/decision, /cases/{id}/report, /phenotypes for the picker. Scoring moved under the case and now answers 503 with the reason when no model registry is reachable, instead of a 500. Verified end to end on a simulated proband (scripts/make-demo-case.sh: real GIAB HG002 background + one real ClinVar 2-star pathogenic NF2 variant). 13 variants called -> 1 coding candidate, and the planted variant ranks first at 0.80 on phenotype 1.00, rarity 1.00 and consequence 1.00, with ClinVar agreeing afterwards. Tests: api 75, ml 18, loader 16, web 27; ruff, mypy, svelte-check, terraform validate, both kustomize overlays and the Nextflow stub run all clean.	2026-09-12 08:30:44 +01:00
Kemal Yaylali	11fb6b3d73	fix: overhaul the platform skeleton, add a serverless deployment track ... An end-to-end audit found the repo could not build, test or run as shipped. This fixes every finding, then adds a Cloud Run track so the demo costs about £1/month idle instead of ~£150. CI (red on its first run) - api: setuptools could not build the package (flat layout with app/ and alembic/) - web: missing @types/node; `vitest run` exited 1 with no test files - pipeline: the stub run needed a gitignored VCF, and no process had a stub block - ruff pinned, mypy configured, DB tests on real Postgres (pgserver locally, service in CI) ML serving (scores were meaningless) - the registered model now carries its own feature engineering and returns predict_proba, so serving sends raw columns and cannot drift from training - resolve by registry alias (stages are deprecated in MLflow 3) and record the real version; re-scoring upserts instead of failing on the unique constraint - ClinVar labels parsed from VEP's lowercase terms Pipeline - exact ref/alt recovered from a CHROM_POS_REF_ALT VCF ID; loading is idempotent - job status reaches running/failed/succeeded, so the UI stops polling dead jobs - DATABASE_URL travels in the environment or a Nextflow secret, never on a command line - VEP cache and plugins staged as inputs; the gcp profile runs tasks on Google Batch Deployment - the API serves /api (matching the ingress); the web app reads its API URL at runtime - migrations run in an init container under a Postgres advisory lock - terraform: custom VPC shared with Batch, private Cloud SQL, API enablement, Workload Identity bindings, Secret Manager, deletion protection - serverless track, now the default: Cloud Run services scaling to zero, a Cloud Run job for the Nextflow driver, and Neon or Cloud SQL behind one DATABASE_URL secret. GKE and Argo remain, behind -var deploy_kubernetes=true. See docs/cloud.md. Correctness and security - 409 on duplicate sample names, 422 on bad paging, natural chromosome ordering, wider VEP text columns, enum dropped on downgrade, the sample's assembly actually used - vcf_uri restricted to gs:// objects or files under the data root, blocking option injection - CORS restricted to configured origins; `make down` no longer deletes volumes Data - docs/data.md records the peer-reviewed, openly licensed sources (GIAB HG002, ClinVar, gnomAD) with citations and an honest evaluation plan; `make data` fetches a chr22 slice Verified: api 50 tests, ml 18, loader 16, web 12; ruff, mypy, svelte-check, terraform validate and both kustomize overlays clean.	2026-09-12 07:21:11 +01:00
kemal	5463f489a3	Initial release: rarelens platform skeleton (AGPL-3.0) ... End-to-end variant interpretation platform for rare genetic disease research: SvelteKit UI, FastAPI + PostgreSQL API, Nextflow/Ensembl VEP pipeline, LightGBM pathogenicity scoring with MLflow, K8s/ArgoCD/GCP infrastructure. Public test data only; no clinical claims.	2026-09-11 16:55:35 +01:00