hitlist
Hitlist 2.0 is a verified evidence store for immune and nonimmune training data. The evidence store guide describes one catalog, query and recipe API across MHC, T-cell, bulk MS, expression, source claims and lineage. Assay results, numeric measurements and contextual claims keep their own grains.
A curated, source-preserving evidence system for model training.
from hitlist import EvidenceStore
store = EvidenceStore.open("/path/to/verified-evidence-package")
print(store.catalog()["schema_version"])
print(store.query("arm_factor", select=["arm_id", "factor_type", "factor_value"],
where={"factor_type": "knockout_genes"}, limit=5).table)
print(store.query("bulk_numeric_measurement",
select=["measurement_id", "quantity_scale", "quantitative_value"],
where={"quantity_scale": "abundance_percentile"}, limit=5).table)
store.materialize("training-recipe.yaml", "/path/to/training-output")
The matching CLI is hitlist evidence catalog, hitlist evidence query,
hitlist evidence materialize and hitlist evidence verify. Each command takes
the same package path. Queries expose logical fields when source meaning and
grain match; every physical typed column is also accessible as native.<column>.
Related values require named joins, and many-valued relations remain separate
evidence rows. The catalog reports direct, related and unavailable fields,
source scope, row counts and ingest requirements. A recipe writes accepted,
excluded and ambiguous rows with counts, reasons, lineage, contributor keys
and a hash-bound manifest. It never rewrites source evidence.
The reference-augmented package adds source-backed mouse species roles and
exact peptide-to-parent alternatives while retaining every original assay and
numeric cell.
Start here
- Evidence store guide: catalog, grain-safe queries, named relationships, recipes and verification.
- Pinned source and count audit: 53 materialized evidence types, 32 reconciled baselines and field disposition.
- Migration history: source generations and scientific decisions behind the current package.
- Legacy 1.x README: historical indexes and commands. Its sample-level and training-readiness descriptions were superseded by the verified corpus audits and should not be used as current labels.
Breaking release
2.0.0 removes several top-level loaders, exporters and static catalog helpers.
Open a verified package through EvidenceStore for current evidence requests;
use the catalog to see each field's actual readiness and required assay fields.
The original source records and typed relation files remain in the verified
evidence package; older module-level APIs remain in the Python distribution
for migration and audit. Training recipes
retain candidate decisions, source contributors and unresolved context, so a
new task does not silently turn evidence into a label.
Metadata
Release files for hitlist 2.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| hitlist-2.0.3.tar.gz | 3.3 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hitlist-2.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.2 MB
Release files / hitlist-2.0.3.tar.gz
| Download URL | hitlist-2.0.3.tar.gz |
|---|---|
| Size | 3.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.6
|
Release files / hitlist-2.0.3-py3-none-any.whl
| Download URL | hitlist-2.0.3-py3-none-any.whl |
|---|---|
| Size | 2.9 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.6
|
Security for hitlist 2.0.3
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