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--- |
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license: apache-2.0 |
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tags: |
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- ppi |
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- proteins |
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- biology |
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- bacteria |
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- stringdb |
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pretty_name: Dataset for predicting protein-protein interactions in bacterial genomes |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Dataset for protein-protein interaction prediction across bacteria (Protein sequences) |
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A dataset of 10,533 bacterial genomes across 6,956 species with protein-protein interaction (PPI) scores for each genome. |
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The genome protein sequences and PPI scores have been extracted from [STRING DB](https://string-db.org/). |
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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. |
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The PPI scores have been extracted using the `combined` score from STRING DB. |
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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` |
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(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 |
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row. See example below in [Usage](#usage) |
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## Usage |
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We recommend loading the dataset in a streaming mode to prevent memory errors. |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("macwiatrak/bacbench-ppi-stringdb-protein-sequences", split="validation", streaming=True) |
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item = next(iter(ds)) |
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# fetch protein sequences from a genome (list of strings) |
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prot_seqs = item["protein_sequences"] |
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# fetch PPI triples labels (i.e. [prot1_index, prot2_index, score]) |
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ppi_triples = item["triples_combined_score"] |
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# get protein seqs and label for one pair of proteins |
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prot1 = prot_seqs[ppi_triples[0][0]] |
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prot2 = prot_seqs[ppi_triples[0][1]] |
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score = ppi_triples[0][2] / 1000 |
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# we recommend binarizing the labels based on the threshold of 0.6 |
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binary_ppi_triples = [ |
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(prot1_index, prot2_index, int((score / 1000) >= 0.6)) for prot1_index, prot2_index, score in ppi_triples |
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] |
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``` |
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## Split |
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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. |
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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 |
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resistance from sequence. |
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--- |
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dataset_info: |
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features: |
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- name: taxid |
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dtype: int64 |
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- name: genome_name |
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dtype: string |
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- name: protein_sequences |
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sequence: string |
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- name: protein_ids |
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sequence: string |
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- name: triples_combined_score |
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sequence: |
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sequence: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 169097180197 |
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num_examples: 7354 |
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- name: validation |
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num_bytes: 27541966357 |
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num_examples: 1091 |
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- name: test |
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num_bytes: 50704862883 |
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num_examples: 2088 |
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download_size: 53853247305 |
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dataset_size: 247344009437 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
