tablassert 7.4.0

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

Tablassert

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