metadata

dataset_info:
  features:
    - name: pmid
      dtype: string
    - name: sentence
      dtype: string
    - name: cancer_type
      dtype: string
    - name: gene
      struct:
        - name: name
          dtype: string
        - name: pos
          sequence: int64
    - name: cancer
      struct:
        - name: name
          dtype: string
        - name: pos
          sequence: int64
    - name: CGE
      dtype: string
    - name: CCS
      dtype: string
    - name: PT
      dtype: string
    - name: IGE
      dtype: string
    - name: expression_change_keyword_1
      struct:
        - name: name
          dtype: string
        - name: pos
          sequence: int64
        - name: type
          dtype: string
    - name: expression_change_keyword_2
      struct:
        - name: name
          dtype: string
        - name: pos
          sequence: int64
        - name: type
          dtype: string
  splits:
    - name: train
      num_bytes: 361666
      num_examples: 821
  download_size: 99496
  dataset_size: 361666
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - biology
  - cancer
  - gene
  - medical
pretty_name: CoMAGC
size_categories:
  - n<1K

Dataset Card for CoMAGC

Dataset Description

Website: http://biopathway.org/CoMAGC/
Paper: CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations

Dataset Summary

CoMAGC Dataset Summary:

CoMAGC is a corpus with multi-faceted annotations of gene-cancer relations. CoMAGC consists of 821 sentences collected from MEDLINE abstracts, and the sentences are about three different types of cancers, or prostate, breast and ovarian cancers. In CoMAGC, a piece of annotation is composed of four semantically orthogonal concepts that together express 1) how a gene changes, 2) how a cancer changes and 3) the causality between the gene and the cancer. The four concepts that constitute the multi-faceted annotation scheme are Change in Gene Expression (CGE), Change in Cell State (CCS), Proposition Type (PT) and Initial Gene Expression level (IGE).

CGE captures whether the expression level of a gene is increased or decreased in a cell
CCS captures the way how the cell changes together with a gene expression level change
- normalTOnormal: The cell or tissue remains as normal after the change in the gene’s expression level.
- normalTOcancer: The cell or tissue acquires cancerous properties as the gene expression level changes; some cancerous properties are strengthened.
- cancerTOcancer: There's no change in the cancerous properties of the cell or tissue despite the change in the expression level of the gene.
- cancerTOnormal: The cell or tissue loses some cancerous properties as the gene expression level changes; some cancerous properties are weakened.
- unidentifiable: The information about whether or not the gene expression level change accompanies cell or tissue state change is not provided.
PT captures whether the causality between the gene expression change and the cell property change
- observation: Cell or tissue change accompanied by the gene expression level change is reported as observed but the causality between the two is not claimed. |
- causality: The causality between the gene expression level change and the cell or tissue change is claimed.
IGE captures the initial expression level of a gene before the change in its expression level
- up-regulated: The initial gene expression level is higher than the expression level of the gene in the normal state.
- down-regulated: The initial gene expression level is lower than the expression level of the gene in the normal state.
- unchanged: The initial gene expression level is comparable to the expression level of the gene in the normal state.
- unidentifiable: The information about the initial gene expression level is not provided. |

The original dataset in XML format is available here: http://biopathway.org/CoMAGC/

We converted the dataset to a JSONL format before pushing the data to the hub.

Languages

The language in the dataset is English.

Dataset Structure

Dataset Instances

An example of 'train' looks as follows:

{
  "pmid": "11722842.s0",
  "sentence": "Isolation and characterization of the major form of human MUC18 cDNA gene and correlation of MUC18 over-expression in prostate cancer cell lines and tissues with malignant progression.",
  "cancer_type": "prostate",
  "gene": {
    "name": "MUC18",
    "pos": [93, 97]
  },
  "cancer": {
    "name": "prostate cancer",
    "pos": [118, 132]
  },
  "CGE": "increased",
  "CCS": "normalTOcancer",
  "PT": "observation",
  "IGE": "unchanged",
  "expression_change_keyword_1": {
    "name": "over-expression",
    "pos": [99, 113],
    "type": "Gene_expression"
  },
  "expression_change_keyword_2": {
    "name": "over-expression",
    "pos": [99, 113],
    "type": "Positive_regulation"
  }
}

Data Fields

pmid: the id of this sentence, a string feature.
sentence: the text of this sentence, a string feature.
cancer_type: the type of cancer in this sentence, a string feature.
gene: gene entity
- pos: character offsets of the gene entity, a list of int32 features.
- name: gene entity text, a string feature.
cancer: cancer entity
- pos: character offsets of the cancer entity, a list of int32 features.
- name: cancer entity text, a string feature.
CGE: change in gene expression, a string feature.
CCS: change in cell state, a string feature.
PT: proposition type, a string feature.
IGE: initial gene expression, a string feature.
expression_change_keyword_1: a dict
- name: keyword text, a string feature.
- pos: character offsets of the keyword, a list of int32 features.
- type: type of the expression change keyword, a string feature.
expression_change_keyword_2: a dict
- name: keyword text, a string feature.
- pos: character offsets of the keyword, a list of int32 features.
- type: type of the expression change keyword, a string feature.

Citation

BibTeX:

@article{lee2013comagc,
  title={CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations},
  author={Lee, Hee-Jin and Shim, Sang-Hyung and Song, Mi-Ryoung and Lee, Hyunju and Park, Jong C},
  journal={BMC bioinformatics},
  volume={14},
  pages={1--17},
  year={2013},
  publisher={Springer}
}

APA:

Lee, H. J., Shim, S. H., Song, M. R., Lee, H., & Park, J. C. (2013). CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations. BMC bioinformatics, 14, 1-17.

Dataset Card Authors

@phucdev