The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets library). If this is not possible, please
open a discussion
for direct help.
Dataset Card for PAIR
This dataset contains all the text annotations we collected and parsed from UniProt Swiss-Prot February 2023 and used to train PAIR from the paper "Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations". You can read more details about PAIR here.
Dataset Details
Dataset Description
Dataset Sources
Uses
Example usage
from datasets import load_dataset
data = load_dataset("mskrt/PAIR", annotation_type="function", trust_remote_code=True)
where annotation_type is one of the 19 annotation types we considered in our work. Here is a list of all the possible annotation types you can load: ['function', 'active_sites', 'activity_regulation'...]
Out-of-Scope Use
This dataset contains text annotations from Swiss-Prot February 2023; our models were trained on all of them. Please be mindful about potential data leakage from time splits/identical protein sequences on any downstream tasks in your setup.
Dataset Structure
[Coming soon]
Dataset Creation
Source Data
This data was collected from the Swiss-Prot checkpoint from February 2023, found here.
Data Collection and Processing
To see how we parsed our data, select an annotation type folder from this link and open the parser.py script.
Who are the source data producers?
The data was originally produced by the Uniprot consortium.
Personal and Sensitive Information
To our knowledge, this dataset does not contain any private information.
Bias, Risks, and Limitations
In general, the dataset is highly imbalanced in terms of how many and what protein sequences in Swiss-Prot have an annotation for a given annotation type. This dataset is sparse
Citation
BibTeX:
@article{duan2024boosting,
title={Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations},
author={Duan, Haonan and Skreta, Marta and Cotta, Leonardo and Rajaonson, Ella Miray and Dhawan, Nikita and Aspuru-Guzik, Alán and Maddison, Chris J},
journal={bioRxiv},
pages={2024--07},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}
Dataset Card Contact
For any issues with this dataset, please contact [email protected] or [email protected]
- Downloads last month
- 41