Implement the matching engine (PLAN §6 Week 3):
- src/scoring.py: weighted-KS/GSEA enrichment, weighted connectivity
score (WTCS, Lamb 2006 / Subramanian 2017), signed NCS normalization,
rank_drugs, and a sickle-pathway mechanistic prior
- tests/test_scoring.py: real reference tests for the scorer (perfect
reversal<null<mimic, same-sign->0, absent-gene invariance) + prior
- week3_scoring.py: score 300 drugs -> ranked_candidates_v1.csv with a
raw ranking and a secondary mechanistic-prior-weighted ranking
Preliminary (formal recovery test is Week 4): hydroxyurea raw rank
40/300 (top 13%, just misses pre-registered top-10%), blended rank 7;
L-glutamine WTCS=0 (ambiguous). Notably anti-inflammatory SCD drugs
cluster in the raw top tier — the engine reverses the inflammation axis,
not the erythroid axis, traceable to the 12% landmark-overlap caveat.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Build the drug profile dataset (PLAN §6 Week 2):
- week2_curate_drugset.py: 300-drug set (2 ground-truth + 32 related-
mechanism + 26 negative-control + 240 random), restricted to
LINCS-scorable compounds, seed=42
- week2_chembl.py: InChIKey->ChEMBL match (145/300), MoA + targets
- week2_lincs_extract.py: cmapPy-slice both Level-5 GCTX phases to 978
landmark genes, mean-aggregate per drug to one consensus signature
- week2_assemble.py: join into drug_profiles_v1.parquet, Tier B (LINCS
single-source), scored flag per PLAN §6 Week 3 task 2
- docs/data_sources.md: drug set composition + LINCS/ChEMBL provenance
Results (all gitignored data): 300/300 drugs scored, both ground-truth
drugs present (hydroxyurea Phase II = CHEMBL467, L-glutamine Phase I).
Key caveat recorded: only 56/477 (12%) of the disease signature genes
are LINCS landmarks, so Week-3 scoring uses a 30-up/26-down query.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
