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README.md

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+# Dataset for protein-protein interaction prediction (Protein sequences)
+
+A dataset of 10,533 bacterial genomes across 6,956 species with protein-protein interaction (PPI) scores for each genome.
+
+The genome protein sequences and PPI scores have been extracted from [STRING DB](https://string-db.org/). 
+Each row contains a set of protein sequences from a genome, ordered by their location on the chromosome and plasmids and a set of associated PPI scores.
+The PPI scores have been extracted using the `combined` score from STRING DB. 
+
+
+The interaction between two proteins is represented by a triple: `[prot1_index, prot2_index, score]`. Where to get a probability score, you must divide the score by `1000` 
+(i.e. if the score is `721` then to get a true score do `721/1000=0.721`). The index of a protein refers to the index of the protein in the `protein_sequences` column of the
+row. See example below in [Usage](#usage)
+
+
+## Usage
+We recommend loading the dataset in a streaming mode to prevent memory errors.
+```python
+from datasets import load_dataset
+
+
+ds = load_dataset("macwiatrak/bacbench-ppi-stringdb-protein-sequences", split="validation", streaming=True)
+item = next(iter(ds))
+
+# fetch protein sequences from a genome (list of strings)
+prot_seqs = item["protein_sequences"]
+# fetch PPI triples labels (i.e. [prot1_index, prot2_index, score])
+ppi_triples = item["triples_combined_score"]
+
+# get protein seqs and label for one pair of proteins
+prot1 = prot_seqs[ppi_triples[0]]
+prot2 = prot_seqs[ppi_triples[1]]
+score = ppi_triples[2] / 1000
+
+# we recommend binarizing the labels based on the threshold of 0.6
+binary_ppi_triples = [
+  (prot1_index, prot2_index, (score / 1000) >= 0.6) for prot1_index, prot2_index, score in ppi_triples
+]
+```
+
+## Split
+
+We provide `train`, `validation` and `test` splits with proportions of `70 / 10 / 20` (%) respectively as part of the dataset. The split was performed randomly at genome level.
+
+See [github repository](https://github.com/macwiatrak/Bacbench) for details on how to embed the dataset with DNA and protein language models as well as code to predict antibiotic
+resistance from sequence.
+
 ---
 dataset_info:
   features: