Junior B.
b731478f5d
Set up the project skeleton per PLAN.md §4: - src/ package: identifiers, disease, drugs, scoring, provenance with pydantic schemas and confidence-tier logic (working); data-pull/compute functions stubbed per their build week - 5 starter notebooks (01-05) with PLAN-referenced steps - tests/test_scoring.py: tier-assignment tests pass; scoring reference test xfail until Week 3 - docs/: recovery_test_report, data_sources, known_limitations skeletons - pyproject.toml (requires-python >=3.11,<3.14), .gitignore, README - data/ tree preserved via .gitkeep; raw/processed/results gitignored Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
69 lines
2.4 KiB
Markdown
69 lines
2.4 KiB
Markdown
# Sickle Cell Repurposing — Recovery Test Report
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> **Status: DRAFT SCAFFOLD — not yet run.** Filled in during Week 4 from
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> `notebooks/05_recovery_test.ipynb`. Target length: ~2 pages, readable by a sceptical
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> pharma scientist in 5 minutes.
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## Pre-registered success criteria
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> ⚠️ **Commit this section to git _before_ running the recovery test** (PLAN.md §8, §10).
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The MVP passes if:
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- Hydroxyurea ranks in the **top 10%** (top 30 of 300), **AND**
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- L-glutamine ranks in the **top 25%** (top 75) **OR** is documented as unscorable due to a
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missing LINCS signature, **AND**
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- At least **4 of 5** negative-control drugs rank in the **bottom half**.
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_Pre-registered on: TBD (date of commit)_
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---
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## Section 1 — Methodology
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_5–6 sentences: what was built, the GEO dataset used, the drug-set composition, and the
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scoring method (CMap connectivity, Lamb 2006 / Subramanian 2017)._
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## Section 2 — Recovery test result
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| Drug | Rank | Percentile | Pass? |
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|---|---|---|---|
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| Hydroxyurea | TBD | TBD | TBD |
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| L-glutamine | TBD | TBD | TBD |
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Negative controls (expected: bottom half):
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| Control drug | Rank | Bottom half? |
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|---|---|---|
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| TBD | TBD | TBD |
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**Overall: PASS / FAIL against pre-registered criteria — TBD**
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## Section 3 — Top 10 candidates
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| Rank | Drug | Score | Known mechanism | Biological plausibility |
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|---|---|---|---|---|
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| 1 | TBD | TBD | TBD | TBD |
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_Note: HDAC inhibitors and broad kinase inhibitors often dominate connectivity rankings due
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to widespread expression effects — flag these honestly (PLAN.md §9.4)._
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## Section 4 — One non-obvious candidate worth investigating
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_A single paragraph on the most interesting result. Language must be careful: this is a
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computational hypothesis to test, not a discovery (PLAN.md §9.7)._
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## Section 5 — Honest limitations
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- Cell-composition confound in whole-blood expression (PLAN.md §9.1)
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- LINCS L1000 cell-line limitations — landmark genes measured mostly in cancer lines (§9.2)
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- Missing signatures (e.g. L-glutamine) (§9.3)
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- No mechanistic validation layer — discovery hypothesis generation, not validated prediction (§9.6)
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## Section 6 — What v2 would fix
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- Cell-type deconvolution of the disease signature
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- Knowledge graph fallback for missing-signature drugs
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- A second disease to test generalization (the real test — sickle cell results do not prove
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the platform generalizes, §9.5)
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