README.md

metadata

license: other
license_name: genereviews
license_link: https://www.ncbi.nlm.nih.gov/books/NBK138602/
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: id
      dtype: string
    - name: ch_id
      dtype: string
    - name: keywords
      list: string
    - name: title
      dtype: string
    - name: authors
      dtype: string
    - name: abstract
      dtype: string
    - name: content
      dtype: string
    - name: references
      list: string
    - name: created_date
      dtype: string
    - name: updated_date
      dtype: string
    - name: revised_date
      dtype: string
    - name: journal
      dtype: string
    - name: source_url
      dtype: string
    - name: publication_types
      list: string
  splits:
    - name: train
      num_bytes: 57554171
      num_examples: 929
  download_size: 15286246
  dataset_size: 57554171
language:
  - en
tags:
  - medical
  - gene
  - reviews
  - medicine
pretty_name: 'GeneReviews '
size_categories:
  - n<1K
task_categories:
  - text-generation

GeneReviews Dataset Extraction

This project extracts text and metadata from GeneReviews® chapters downloaded from NCBI Bookshelf and creates a structured dataset in Hugging Face format.

Overview

GeneReviews® is an international point-of-care resource for clinicians, providing clinically relevant and medically actionable information for inherited conditions. This project processes the XML files from the GeneReviews database and creates a structured dataset suitable for machine learning and research applications.

📈 Dataset Statistics:

• Total Records: 929 GeneReviews chapters
• Average Abstract Length: 899.6 characters
• Average Content Length: 56,377.9 characters
• Total References: 13,683 references across all chapters
• Average References per Chapter: 14.7
• Chapters with >100 references: 12 chapters
• Total Keywords: 9,616
• Unique Keywords: 6,824

Source Information

• Source: GeneReviews® on NCBI Bookshelf
• Publisher: University of Washington, Seattle
• ISSN: 2372-0697
• Content Type: Clinical reviews of genetic conditions
• License: Open access for noncommercial research purposes

Dataset Structure

Each record in the dataset contains the following fields:

Field	Type	Description
id	string	Unique chapter identifier
ch_id	string	Chapter ID (as you renamed it)
title	string	Chapter title
authors	string	Comma-separated author names
journal	string	"GeneReviews®"
abstract	string	Chapter abstract/summary only
content	string	Chapter body content only (excluding abstract)
references	array	Array of reference citations
keywords	array	Keywords and terms
source_url	string	Link to GeneReviews resource
publication_types	array	["Review", "Clinical Review"]
created_date	string	Creation date
updated_date	string	Last update date
revised_date	string	Revision date

Files

• extract_genereviews.py: Main extraction script
• load_genereviews_dataset.py: Script to load and demonstrate the dataset
• requirements.txt: Python dependencies
• genereviews_dataset/: Hugging Face dataset directory
• genereviews_dataset.json: JSON version of the dataset

Installation

1. Install the required dependencies:

pip install -r requirements.txt

Usage

from datasets import load_from_disk

# Load the dataset
dataset = load_from_disk("genereviews_dataset")

# Access by chapter
record = dataset[0]
chapter_id = record['ch_id']

# Access separated content
abstract = record['abstract']      # Only the abstract
content = record['content']        # Only the body content
references = record['references']  # Array of reference citations

Search for Specific Conditions

# Search for cystic fibrosis
cf_records = dataset.filter(lambda x: "cystic fibrosis" in x['title'].lower())

# Search for cancer-related content
cancer_records = dataset.filter(lambda x: "cancer" in x['content'].lower())

Analyze Publication Dates

# Find recently updated chapters
recent_updates = dataset.filter(lambda x: "2024" in x['updated_date'])

Extract Keywords

# Get all unique keywords
all_keywords = set()
for record in dataset:
    all_keywords.update(record['keywords'])

Citation

When using this dataset, please cite:

GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2025.
Available from: https://www.ncbi.nlm.nih.gov/books/NBK1116/

License

This dataset is derived from GeneReviews®, which is owned by the University of Washington. Permission is granted to reproduce, distribute, and translate copies of content materials for noncommercial research purposes only, provided that proper attribution is given.