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GDA

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
medical
biology
relation-classification
relation-extraction
gene
disease
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pandas
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metadata
dataset_info:
  features:
    - name: NofPmids
      dtype: float64
    - name: NofSnps
      dtype: float64
    - name: associationType
      dtype: string
    - name: diseaseId
      dtype: string
    - name: diseaseName
      dtype: string
    - name: diseaseType
      dtype: string
    - name: disease_mention
      dtype: string
    - name: geneId
      dtype: string
    - name: geneSymbol
      dtype: string
    - name: gene_mention
      dtype: string
    - name: originalSource
      dtype: string
    - name: pmid
      dtype: int64
    - name: raw_sentence
      dtype: string
    - name: score
      dtype: float64
    - name: sentence
      dtype: string
    - name: source
      dtype: string
  splits:
    - name: train
      num_bytes: 1907978
      num_examples: 4000
    - name: validation
      num_bytes: 1134075
      num_examples: 2400
    - name: test
      num_bytes: 756401
      num_examples: 1600
  download_size: 2013053
  dataset_size: 3798454
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:
  - medical
  - biology
  - relation-classification
  - relation-extraction
  - gene
  - disease
pretty_name: GDA
size_categories:
  - 1K<n<10K

Dataset Card for GDA

Dataset Description

Dataset Summary

GDA Dataset Summary:

Nourani and Reshadata (2020) developed a dataset called GDA corpus as a sentence-level evaluation dataset for extracting the association between genes and diseases based on some efficient databases. They used DisGeNET, a database of Gene-Disease-Associations (GDAs), and PubTator to retrieve biomedical texts (PubMed abstracts). Using PubTator, they found all the PMIDs containing at least one gene and disease name. Samples of the true class were extracted from DisGeNET, considering only curated associations. For the creation of non-associated or false samples, a systematic three-step filtering process was used to ensure high-quality annotations. This included the exclusion of known associations from DisGeNET and CTD, as well as a linguistic filtering step to remove sentences that linguistically imply an association. GDA was constructed automatically and contains 8000 sentences with 1904 and 3635 unique diseases and genes respectively.

Languages

The language in the dataset is English.

Dataset Structure

Dataset Instances

An example of 'train' looks as follows:

{
  "NofPmids": 2.0,
  "NofSnps": 0.0,
  "associationType": "Biomarker",
  "diseaseId": "C0043459",
  "diseaseName": "Zellweger Syndrome",
  "diseaseType": "disease",
  "disease_mention": "H-02",
  "geneId": "5194",
  "geneSymbol": "PEX13",
  "gene_mention": "PEX13",
  "originalSource": "CTD_human",
  "pmid": 10332040,
  "raw_sentence": "We now have evidence that the complete human cDNA encoding Pex13p, an SH3 protein of a docking factor for the peroxisome targeting signal 1 receptor (Pex5p), rescues peroxisomal matrix protein import and its assembly in fibroblasts from PBD patients of complementation group H. In addition, we detected mutations on the human PEX13 cDNA in two patients of group H. A severe phenotype of a ZS patient (H-02) was homozygous for a nonsense mutation, W234ter, which results in the loss of not only the SH3 domain but also the putative transmembrane domain of Pex13p.",
  "score": 0.400549453568426,
  "sentence": "We now have evidence that the complete human cDNA encoding Pex13p, an SH3 protein of a docking factor for the peroxisome targeting signal 1 receptor (Pex5p), rescues peroxisomal matrix protein import and its assembly in fibroblasts from PBD patients of complementation group H. In addition, we detected mutations on the human <span class=\"gene\" id=\"10332040-3-326-331\">PEX13</span> cDNA in two patients of group H. A severe phenotype of a <span class=\"disease\" id=\"10332040-3-389-391\">ZS</span> patient (<span class=\"disease\" id=\"10332040-3-401-405\">H-02</span>) was homozygous for a nonsense mutation, W234ter, which results in the loss of not only the SH3 domain but also the putative transmembrane domain of Pex13p.",
  "source": "CTD_human;ORPHANET"
}

Data Fields

Here's the Data Fields section for the GDA corpus based on the dataset features provided:

  • NofPmids: the number of PubMed IDs related to the gene-disease association, stored as a float64 feature.
  • NofSnps: the number of single nucleotide polymorphisms (SNPs) related to the gene-disease association, stored as a float64 feature.
  • associationType: the type of association (e.g., Negative, Biomarker, Therapeutic) between the gene and the disease, a string feature.
  • diseaseId: the unique identifier for the disease discussed, a string feature.
  • diseaseName: the name of the disease, a string feature.
  • diseaseType: the type of the disease (e.g., disease, group, phenotype), a string feature.
  • disease_mention: the specific mention of the disease within the source text, a string feature.
  • geneId: the unique identifier for the gene discussed, a string feature.
  • geneSymbol: the symbol representing the gene, a string feature.
  • gene_mention: the specific mention of the gene within the source text, a string feature.
  • originalSource: the original source, a string feature.
  • pmid: the PubMed ID associated with the citation from which the sentence is derived, an int64 feature.
  • raw_sentence: the original sentence from the source document, a string feature.
  • score: a score reflecting the confidence or relevance of the association between the gene and the disease, stored as a float64 feature.
  • sentence: the sentence with span annotation, a string feature.
  • source: the database or repository from which the association data was taken, a string feature.

Citation

BibTeX:

@article{NOURANI2020110112,
title = {Association extraction from biomedical literature based on representation and transfer learning},
journal = {Journal of Theoretical Biology},
volume = {488},
pages = {110112},
year = {2020},
issn = {0022-5193},
doi = {https://doi.org/10.1016/j.jtbi.2019.110112},
url = {https://www.sciencedirect.com/science/article/pii/S0022519319304813},
author = {Esmaeil Nourani and Vahideh Reshadat},
keywords = {Gene-Disease Association Extraction, Attention Mechanism, BioBERT}
}

APA:

  • Nourani, E., & Reshadat, V. (2020). Association extraction from biomedical literature based on representation and transfer learning. Journal of Theoretical Biology, 488, 110112. https://doi.org/10.1016/j.jtbi.2019.110112

Dataset Card Authors

@phucdev