metadata
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, found in
Cracking the black box of deep sequence-based protein–protein interaction prediction
and
Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65
Description
This is a balanced binary protein-protein interaction dataset with positives from HIPPIE and paritioned with KaHIP. 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 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
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.