README.md

---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: relation
    dtype: string
  - name: h
    struct:
    - name: id
      dtype: int64
    - name: name
      dtype: string
    - name: pos
      sequence: int64
  - name: t
    struct:
    - name: id
      dtype: string
    - name: name
      dtype: string
    - name: pos
      sequence: int64
  splits:
  - name: train
    num_bytes: 54491244
    num_examples: 178264
  - name: validation
    num_bytes: 6118764
    num_examples: 20193
  - name: test
    num_bytes: 6168865
    num_examples: 20516
  download_size: 35878376
  dataset_size: 66778873
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
task_categories:
- text-classification
language:
- en
tags:
- biology
- relation-classification
- medical
- relation-extraction
- gene
- disease
- gda
pretty_name: TBGA
size_categories:
- 100K<n<1M
---
# Dataset Card for TBGA

## Dataset Description

- **Repository:** https://github.com/GDAMining/gda-extraction
- **Paper:** [TBGA: a large‑scale Gene‑Disease Association dataset for Biomedical RelationExtraction](https://link.springer.com/epdf/10.1186/s12859-022-04646-6?sharing_token=qgaQQs92ZxFpodts5HhcmW_BpE1tBhCbnbw3BuzI2RNBkapcoPX8TYwxqVikGDmcarZHWjFQGawSFYjAFhD3cB50vnZY-JefC9csY__WaxOMsnqCn5_cyZrmWMAyl_T3CruatRTM1QvUt6DbcOiPnb7cks1YDxyHWkekMqdYB1A%3D)

#### Dataset Summary

<!-- Provide a quick summary of the dataset. -->
TBGA is a comprehensive dataset created for the purpose of Gene-Disease Association (GDA) extraction, generated from over 700,000 publications. 
It features more than 200,000 instances and 100,000 unique gene-disease pairs. 
Each instance in the dataset includes the specific sentence from which the GDA was extracted, the extracted GDA itself, and detailed information about the gene-disease pair involved. 
This dataset was semi-automatically annotated by Marchesin and Silvello using data sourced from the DisGeNET database, which houses one of the most extensive collections of genes and variants associated with human diseases.
The dataset follows the OpenNRE format and contains the following relations:
```json
{"NA": 0, "therapeutic": 1, "biomarker": 2, "genomic_alterations": 3}
```

### Languages

The language in the dataset is English.


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

### Dataset Instances

An example of 'train' looks as follows:
```json
{
  "text": "A monocyte chemoattractant protein-1 gene polymorphism is associated with occult ischemia in a high-risk asymptomatic population.",
  "relation": "NA",
  "h": {
    "id": 6347,
    "name": "CCL2",
    "pos": [2, 34]
  },
  "t": {
    "id": "C0231221",
    "name": "Asymptomatic",
    "pos": [105, 12]
  }
}
```


### Data Fields

- `text`: the text of this example, a `string` feature.
- `h`: the gene entity
    - `id`: NCBI Entrez ID associated with the gene entity, a `string` feature.
    - `pos`: list consisting of starting position and length of the gene mention withintext, a list of `int32` features.
    - `name`: NCBI official gene symbol associated with the gene entity (not the text of the mention), a `string` feature.
- `t`: the disease entity
    - `id`: UMLS Concept Unique Identifier (CUI) associated with the disease entity, a `string` feature.
    - `pos`: list consisting of starting position and length of the disease mention withintext, a list of `int32` features.
    - `name`:  UMLS preferred term associated with the disease entity (not the text of the mention), a `string` feature.
- `relation`: a class label associated with the given GDA.


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@article{marchesin-silvello-2022,
  title = "TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction",
  author = "S. Marchesin and G. Silvello",
  journal = "BMC Bioinformatics",
  year = "2022",
  url = "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04646-6",
  doi = "10.1186/s12859-022-04646-6",
  volume = "23",
  number = "1",
  pages = "111"
}
```

**APA:**

- Marchesin, S., & Silvello, G. (2022). TBGA: A large-scale Gene-Disease Association dataset for Biomedical Relation Extraction. BMC Bioinformatics, 23(1), 111. https://doi.org/10.1186/s12859-022-04646-6

## Dataset Card Authors

[@phucdev](https://github.com/phucdev)