bernett_gold_ppi / README.md
lhallee's picture
Update README.md
bd0fed0 verified
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.