Reverso

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Author	SHA1	Message	Date
Junior B.	47b0094079	Week 2: 300-drug profiles with LINCS signatures + ChEMBL 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>	2026-06-23 22:25:00 +02:00
Junior B.	c7b6649d31	Week 1: Tier-A sickle cell signature via 2-study concordance Implement and run the Week 1 disease-signature pipeline: - src/disease.py: Welch t-test + BH DE (microarray), probe->symbol collapse, cross-study concordance filter, 2-study provenance schema - scripts/week1_explore.py: download GSE35007 + GSE16728, DE + concordance - scripts/week1_finalize.py: mygene ID mapping + persist signature - tests/test_disease.py: synthetic-data tests for DE/collapse/concordance - docs/data_sources.md: chosen datasets, group defs, reproduction steps Result: sickle_cell_signature_v1.json (gitignored), Tier A, 250 up / 227 down genes from 671 concordant (GSE35007 Illumina whole blood SS/AA + GSE16728 Affymetrix whole blood patient/control). Documented caveats: missing HbF axis (globin depletion) and reticulocyte composition confound. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-23 20:43:54 +02:00
Junior B.	b731478f5d	Scaffold Reverso MVP pipeline structure 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>	2026-06-23 20:20:09 +02:00

Author

SHA1

Message

Date

Junior B.

47b0094079

Week 2: 300-drug profiles with LINCS signatures + ChEMBL

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>

2026-06-23 22:25:00 +02:00

Junior B.

c7b6649d31

Week 1: Tier-A sickle cell signature via 2-study concordance

Implement and run the Week 1 disease-signature pipeline:
- src/disease.py: Welch t-test + BH DE (microarray), probe->symbol
  collapse, cross-study concordance filter, 2-study provenance schema
- scripts/week1_explore.py: download GSE35007 + GSE16728, DE + concordance
- scripts/week1_finalize.py: mygene ID mapping + persist signature
- tests/test_disease.py: synthetic-data tests for DE/collapse/concordance
- docs/data_sources.md: chosen datasets, group defs, reproduction steps

Result: sickle_cell_signature_v1.json (gitignored), Tier A, 250 up /
227 down genes from 671 concordant (GSE35007 Illumina whole blood SS/AA +
GSE16728 Affymetrix whole blood patient/control). Documented caveats:
missing HbF axis (globin depletion) and reticulocyte composition confound.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-06-23 20:43:54 +02:00

Junior B.

b731478f5d

Scaffold Reverso MVP pipeline structure

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>

2026-06-23 20:20:09 +02:00

3 Commits