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Reverso/notebooks/04_connectivity_scoring.ipynb
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

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 04 \u2014 Connectivity scoring\n",
"\n",
"Week 3 (PLAN.md \u00a76). CMap-style connectivity scoring of every drug against the sickle cell signature. Strongly negative connectivity = strong reversal = candidate.\n\nOutputs `data/results/ranked_candidates_v1.csv`. Also build the secondary mechanistically-weighted ranking. Document the gene-overlap count; mark signature-less drugs as 'not scored' rather than dropping them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '..') # import the src package from notebooks/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from src import scoring\n",
"\n",
"# Load signature + drug profiles, then:\n",
"# ranking = scoring.rank_drugs(up, down, drug_profiles)\n",
"# scoring.persist_ranking(ranking) -> data/results/ranked_candidates_v1.csv"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}