--- 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.