metadata

license: apache-2.0
task_categories:
  - text-classification
  - feature-extraction
language:
  - en
tags:
  - pharmacogenomics
  - biomedical
  - variant-drug-associations
  - literature-mining
  - genomics
size_categories:
  - 1K<n<10K
source_datasets:
  - original
multilinguality:
  - monolingual
pretty_name: AutoGKB Annotation Benchmark
dataset_info:
  features:
    - name: pmcid
      dtype: string
    - name: article_title
      dtype: string
    - name: article_path
      dtype: string
    - name: article_text
      dtype: string
    - name: variant_annotation_id
      dtype: int64
    - name: variant_haplotypes
      dtype: string
    - name: gene
      dtype: string
    - name: drugs
      dtype: string
    - name: pmid
      dtype: int64
    - name: phenotype_category
      dtype: string
    - name: significance
      dtype: string
    - name: notes
      dtype: string
    - name: sentence
      dtype: string
    - name: alleles
      dtype: string
    - name: specialty_population
      dtype: string
    - name: metabolizer_types
      dtype: string
    - name: is_plural
      dtype: string
    - name: is_is_not_associated
      dtype: string
    - name: direction_of_effect
      dtype: string
    - name: pd_pk_terms
      dtype: string
    - name: multiple_drugs_and_or
      dtype: string
    - name: population_types
      dtype: string
    - name: population_phenotypes_or_diseases
      dtype: string
    - name: multiple_phenotypes_or_diseases_and_or
      dtype: string
    - name: comparison_alleles_or_genotypes
      dtype: string
    - name: comparison_metabolizer_types
      dtype: string
  splits:
    - name: train
      num_examples: 3124
    - name: validation
      num_examples: 796
    - name: test
      num_examples: 596

AutoGKB Annotation Benchmark

Dataset Description

The AutoGKB Annotation Benchmark is a comprehensive dataset designed to evaluate models' ability to extract pharmacogenomic variant-drug associations from scientific literature. This ground truth values for this data were compiled by reviewers from PharmGKB. This benchmark addresses the critical need for automated systems that can identify genetic variants, associated drugs, and their clinical relationships from biomedical texts.

Dataset Summary

This dataset contains 4,516 annotations extracted from 1,431 unique scientific papers (PMIDs), covering a wide range of pharmacogenomic relationships. Each annotation includes detailed information about genetic variants, drugs, phenotype categories, population specifics, and statistical associations. The goal of this dataset is to benchmark the process of extracting information a PubMed paper on pharmacogenomics. The annotation system should be able to understand and extract the key information that is represented in the dataset.

Languages

The dataset is in English (en).

Dataset Structure

Data Instances

Each example contains:

Core annotation fields: Variant, gene, drug, PMID, phenotype category
Association details: Significance, direction of effect, comparison data
Population information: Specialty populations, metabolizer types
Full text: Complete scientific article in markdown format

Example:

{
  "pmcid": "PMC6714673",
  "article_title": "Warfarin Dose Model for the Prediction of Stable Maintenance Dose in Indian Patients",
  "article_path": "articles/PMC6714673.md",
  "article_text": "# Warfarin Dose Model for the Prediction of Stable Maintenance Dose in Indian Patients\n\n## Abstract\n\nWarfarin is a commonly used anticoagulant...",
  "variant_annotation_id": 1449192282,
  "variant_haplotypes": "rs1799853",
  "gene": "CYP2C9",
  "drugs": "warfarin",
  "pmid": 28049362,
  "phenotype_category": "Dosage",
  "significance": "yes",
  "sentence": "Genotype CT is associated with decreased dose of warfarin as compared to genotype CC.",
  "alleles": "CT",
  "is_is_not_associated": "Associated with",
  "direction_of_effect": "decreased",
  "pd_pk_terms": "dose of",
  "comparison_alleles_or_genotypes": "CC"
}

Data Fields

Core Fields

pmcid: PubMed Central identifier of the source article
article_title: Title of the source scientific article
article_path: Relative path to the article file (markdown format)
article_text: Full text of the scientific article in markdown format
variant_annotation_id: Unique identifier for each annotation
variant_haplotypes: Genetic variant identifier (e.g., rs numbers, haplotypes)
gene: Gene symbol (e.g., CYP2D6, ABCB1)
drugs: Drug name(s) involved in the association
pmid: PubMed identifier of the source article

Phenotype Information

phenotype_category: Type of effect (Efficacy, Toxicity, Dosage, Metabolism/PK, etc.)
significance: Statistical significance (yes/no/not stated)
sentence: Key sentence describing the association
notes: Additional context or study details

Association Details

is_is_not_associated: Whether variant is associated with outcome
direction_of_effect: Direction of association (increased/decreased)
pd_pk_terms: Pharmacodynamic/pharmacokinetic terms
alleles: Specific alleles involved

Population Context

specialty_population: Specific patient populations
population_types: General population categories
population_phenotypes_or_diseases: Diseases or conditions
metabolizer_types: CYP metabolizer classifications

Comparison Data

comparison_alleles_or_genotypes: Reference genotypes for comparison
comparison_metabolizer_types: Reference metabolizer types

Data Splits

Split	Annotations	Unique Papers
Train	3,124	1,001
Validation	796	215
Test	596	215

Total: 4,516 annotations across 1,431 papers

Dataset Creation

Curation Rationale

This benchmark was created to address the growing need for automated pharmacogenomic knowledge extraction. With the rapid expansion of pharmacogenomic literature, manual curation becomes increasingly challenging. This dataset provides a standardized evaluation framework for developing and comparing automated extraction systems.

Source Data

Initial Data Collection and Normalization

The dataset is derived from peer-reviewed scientific publications in the pharmacogenomics domain. Articles were selected based on their content related to genetic variant-drug associations and clinical outcomes.

Who are the source language producers?

The source texts are scientific articles authored by researchers in pharmacogenomics, clinical pharmacology, and related biomedical fields, published in peer-reviewed journals.

Annotations

Annotation process

Annotations were created by domain experts following a comprehensive schema covering:

Genetic variant identification and standardization
Drug name normalization
Phenotype categorization using controlled vocabularies
Population and study context extraction
Statistical association characterization

Who are the annotators?

The annotations were created by experts in pharmacogenomics and biomedical informatics with specialized knowledge in genetic variant-drug associations.

Personal and Sensitive Information

The dataset contains only published scientific literature and does not include personal or sensitive information about individuals.

Considerations for Using the Data

Social Impact of Dataset

This dataset supports the development of automated systems for pharmacogenomic knowledge extraction, which can:

Accelerate precision medicine: Enable faster identification of clinically relevant variant-drug associations
Support clinical decision-making: Facilitate evidence-based prescribing decisions
Advance research: Enable large-scale analysis of pharmacogenomic literature

Discussion of Biases

Potential biases in the dataset may include:

Publication bias: Overrepresentation of statistically significant results
Population bias: Uneven representation of different ethnic populations in source studies
Drug bias: Focus on commonly studied drugs and variants
Temporal bias: Emphasis on more recent research publications

Other Known Limitations

Coverage: Represents approximately 33.6% of original pharmacogenomic annotations from the source database
Language: Limited to English-language publications
Domain scope: Focused specifically on pharmacogenomics, may not generalize to other biomedical domains
Text quality: Depends on the quality of PDF-to-text conversion for source articles

Additional Information

Dataset Curators

AutoGKB Team

Licensing Information

This dataset is released under the Apache License 2.0.

Citation Information

@misc{autogkb_annotation_benchmark_2025,
  title={AutoGKB Annotation Benchmark},
  author={Shlok Natarajan},
  year={2025},
  note={A benchmark for pharmacogenomic variant-drug annotation extraction from scientific literature}
}

Contributions

This dataset contributes to the biomedical NLP community by providing:

A standardized benchmark for pharmacogenomic information extraction
High-quality annotations with detailed schema
Full-text articles paired with structured annotations
Evaluation metrics and baseline models for comparison

Usage Examples

Loading the Dataset

from datasets import load_dataset

# Load the dataset from Hugging Face Hub
dataset = load_dataset("autogkb/autogkb-annotation-benchmark")

# Access different splits
train_data = dataset["train"]
val_data = dataset["validation"] 
test_data = dataset["test"]

# Example: Get all efficacy-related annotations
efficacy_examples = train_data.filter(
    lambda x: "Efficacy" in x["phenotype_category"]
)

# Example: Access article text for a specific annotation
first_example = train_data[0]
print(f"PMC ID: {first_example['pmcid']}")
print(f"Article Title: {first_example['article_title']}")
print(f"Gene: {first_example['gene']}")
print(f"Drug: {first_example['drugs']}")
print(f"Full Article Text: {first_example['article_text'][:500]}...")

Evaluation

The dataset includes evaluation scripts for measuring:

Field-level exact match accuracy
Overall accuracy across core fields
Phenotype-specific performance

# Run baseline model
python baseline_model.py val baseline_predictions.tsv

# Evaluate predictions
python evaluate.py baseline_predictions.tsv val/annotations.tsv --output results.json

File Structure

autogkb/
├── articles/             # Full article texts in markdown format
│   ├── PMC10038974.md
│   ├── PMC10085626.md
│   └── ...              # 1,431 articles total
├── train.jsonl          # Training annotations (3,124 examples)
├── val.jsonl            # Validation annotations (796 examples)
├── test.jsonl           # Test annotations (596 examples)
├── autogkb_annotation_benchmark.py  # HuggingFace dataset script
├── dataset_infos.json   # Dataset metadata
├── dataset_statistics.json  # Dataset statistics
├── LICENSE              # Apache 2.0 license
└── README.md           # This file