MedNLPCombined

Dataset Description

MedNLPCombined is a collected repository of medical Natural Language Processing (NLP) datasets, primarily focused on Chemical-Disease Relations (CDR), Toxicogenomics, and Gene Interactions. This repository is designed to facilitate research in Named Entity Recognition (NER) and Relation Extraction (RE) within the biomedical domain.

The repository currently includes three major components:

1. BioCreative V CDR (BC5CDR) Task Corpus
2. Comparative Toxicogenomics Database (CTD) Derived Data
3. ChemDisGene Dataset

Repository Structure

The dataset is organized into the following directories:

1. bc5cdr/

Contains the BioCreative V CDR corpus resources.

• data/: The core dataset files.
  • benchmark/: Evaluation/test sets.
  • training/: Training and development sets.
• related_documents/: Documentation and guidelines for the BC5CDR task.
  • BC5CDR.corpus.pdf
  • BC5CDR.overview.pdf
  • bc5_CDR_data_guidelines.pdf

2. CTD/

Contains data derived from the Comparative Toxicogenomics Database.

• CTD.zip: A compressed archive likely containing processed abstracts, relationships, and entity annotations derived from CTD.
  • Note: You may need to unzip this file to access the raw .tsv or .txt files.

3. ChemDisGene/

Contains the ChemDisGene dataset for distant supervision of biomedical relationships.

• data/: The core dataset files.
• related_documents/: Documentation and guidelines.
  • AnnotationGuidelines.pdf

Dataset Details

BioCreative V CDR (BC5CDR)

The BC5CDR corpus consists of 1,500 PubMed articles with annotated chemical and disease entities, as well as their chemical-induced disease (CID) relations. It was created for the BioCreative V challenge.

• Entities: Chemicals, Diseases
• Relations: Chemical-Induced Disease (CID)

Comparative Toxicogenomics Database (CTD)

The CTD data provides manually curated information about chemical-gene/protein interactions, chemical-disease and gene-disease relationships. This component of the repository is likely a snapshot or a derived subset focusing on specific interaction types (e.g., chemical-disease-gene networks).

ChemDisGene

ChemDisGene is a large-scale, distant-supervision dataset for extracting biomedical relationships between chemicals, diseases, and genes. It provides a valuable resource for training models on a broader range of biomedical interactions.

Usage

To use this dataset in your Python project:

from datasets import load_dataset

# Example loading (if configured with a loading script, otherwise access files directly)
# dataset = load_dataset("username/MedNLPCombined")

For local usage, clone the repository:

git clone https://huggingface.co/username/MedNLPCombined

Citation

If you use these datasets, please cite the original authors:

BC5CDR:

@article{li2016biocreative,
  title={BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
  author={Li, Jiao and Sun, Yueping and Johnson, Robin J and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Lu, Zhiyong},
  journal={Database},
  volume={2016},
  year={2016},
  publisher={Oxford Academic}
}

CTD: Please refer to the CTD citation policy.

ChemDisGene:

@inproceedings{zhang-etal-2022-distant,
    title = "A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes",
    author = "Zhang, Dongxu  and
      Mohan, Sunil  and
      Torkar, Michaela  and
      McCallum, Andrew",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.666",
    pages = "6205--6214",
}

License

This repository is licensed under Apache 2.0. Please also adhere to the specific license agreements of the original datasets (BC5CDR, CTD, and ChemDisGene) if applicable.