metadata

dataset_info:
  features:
    - name: seq
      dtype: string
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 1044473
      num_examples: 2072
    - name: test
      num_bytes: 729662
      num_examples: 1344
  download_size: 1546309
  dataset_size: 1774135
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: apache-2.0
task_categories:
  - text-classification
tags:
  - chemistry
  - biology
  - medical
size_categories:
  - 1K<n<10K

Dataset Card for Antibiotic Resistance Dataset

Dataset Summary

Antibiotic resistance refers to the ability of bacteria and other microorganisms to resist the effects of an antibiotic to which they are once sensitive. In this task, an input protein sequence is categorized according to which of 19 antibiotics it is resistant to. Thus, the scope of antibiotic drug development and research could be explored as an understanding in this topic is accumulated.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and an integer label indicating which antibiotics a protein sequence is categorized to. See the antibiotic resistance dataset viewer to explore more examples.

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':6}

The average for the seq and the label are provided below:

Feature	Mean Count
seq	507

Data Fields

seq: a string containing the protein sequence
label: an integer label indicating which antibiotics a protein sequence is categorized to.

Data Splits

The antibiotic resistance dataset has 2 splits: train and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	2,072
Test	1,344

Source Data

Initial Data Collection and Normalization

The Dataset used in this task is curated by CARD.

Citation

If you find our work useful, please consider citing the following paper:

@misc{chen2024xtrimopglm,
  title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
  author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
  year={2024},
  eprint={2401.06199},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  note={arXiv preprint arXiv:2401.06199}
}