Tablassert

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Extract knowledge assertions from tabular data into NCATS Translator-compliant KGX NDJSON — declaratively, with entity resolution built in and optional quality control.

pip install tablassert
tablassert build config.yaml

Full Documentation — installation guides, tutorials, configuration reference, and API docs.

Installation

pip install tablassert

Base install includes web and Excel support. Optional extras are available for CPU compatibility and QC runtime selection:

pip install "tablassert[rt]"       # Polars build for CPUs without required instructions
pip install "tablassert[qc]"       # Enable QC with CPU ONNX Runtime
pip install "tablassert[qc-cuda]"  # Enable QC with CUDA ONNX Runtime on GPU 0

QC is disabled by default at the graph level. Set qc: true in a graph config to enable the audit stage.

Docker 
docker pull ghcr.io/skyeav/tablassert:latest

docker run --rm \
  -v /path/to/config:/data \
  -v /path/to/datassert:/datassert \
  ghcr.io/skyeav/tablassert:latest \
  build /data/graph-config.yaml

Quick Demo

from pathlib import Path
from tablassert.lib import resolve_many

# Resolve gene names to CURIEs against a datassert database
results = resolve_many(
    col="gene",
    entities=["TP53", "BRCA1", "EGFR"],
    datassert=Path("/path/to/datassert"),
    taxon="9606",
)

for row in results:
    print(f"{row['original gene']} → {row['gene']} ({row['gene name']})")
# TP53 → HGNC:11998 (TP53)
# BRCA1 → HGNC:1100 (BRCA1)
# EGFR → HGNC:3236 (EGFR)

Point resolve_many() at a datassert database and resolve any iterable of entity strings to CURIEs — no LazyFrame setup, NLP preprocessing, or DuckDB connection management required. For full pipeline builds with YAML configuration, use tablassert build config.yaml.

Key Features

  • Declarative Configuration — YAML-based, no code required
  • Entity Resolution — Maps text to biological entities (genes, diseases, chemicals)
  • Quality Control — Optional three-stage validation (exact → fuzzy → BERT embeddings)
  • KGX Compliance — NCATS Translator-compatible NDJSON output
  • Performance — Lazy evaluation pipelines with Polars and DuckDB-accelerated entity resolution

Contributing

See CONTRIBUTING.md for development setup, code style, and pull request guidelines.

License

Apache License 2.0

Contributors

Skye Lane Goetz — Institute for Systems Biology, CalPoly SLO

Gwênlyn Glusman — Institute for Systems Biology

Jared C. Roach — Institute for Systems Biology

Project links

Data verified by PyPI on Jun 30, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (8.2.0)

1 maintainer

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Avatar for SkyeAv from gravatar.com SkyeAv

Credits

Author: Skye Lane Goetz

GitHub Statistics

Data verified by PyPI on Jun 30, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.11

Provides Extra

qc qc-cuda rt

Tags

bioinformatics data quality control declarative pipeline entity resolution kgx knowledge graph ncats translator ner tablassert table mining yaml configuration

Classifiers

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