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dataset_info:
features:
- name: A
dtype: string
- name: B
dtype: string
- name: labels
dtype: int8
- name: SeqA
dtype: string
- name: SeqB
dtype: string
splits:
- name: train
num_bytes: 271796192
num_examples: 163192
- name: valid
num_bytes: 73318294
num_examples: 59260
- name: test
num_bytes: 57573817
num_examples: 52048
download_size: 291089780
dataset_size: 402688303
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
# Leakage-free "gold" standard PPI dataset
From [Bernett, et al](https://figshare.com/articles/dataset/PPI_prediction_from_sequence_gold_standard_dataset/21591618), found in
Cracking the black box of deep sequence-based protein–protein interaction prediction
* [paper](https://academic.oup.com/bib/article/25/2/bbae076/7621029)
* [code](https://github.com/daisybio/data-leakage-ppi-prediction)
and
Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65
* [paper](https://www.biorxiv.org/content/10.1101/2025.01.23.634454v1)
* [code](https://github.com/daisybio/PPI_prediction_study)
## Description
This is a balanced binary protein-protein interaction dataset with positives from [HIPPIE](https://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/) and paritioned with [KaHIP](https://kahip.github.io/). There are no sequence overlaps in splits, furthermore, they are split based on a maximum CD-HIT of 40% pairwise sequence similarity. Node degree bias was also reduced.
Note:
[Q96PU5](https://www.uniprot.org/uniprotkb/Q96PU5) is not located in the provided SwissProt fasta file but is used in the train split several times. We added this before data processing so no rows were dropped.
## Example use
```python
def get_ppi_data():
data = load_dataset("Synthyra/bernett_gold_ppi").shuffle(seed=42)
data = data.remove_columns(['A', 'B'])
return data['train'], data['valid'], data['test']
```
## Please cite
Please cite their figshare dataset and papers if you use this dataset.
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