| | --- |
| | license: mit |
| | configs: |
| | - config_name: dataset |
| | data_files: "dataset.csv" |
| | --- |
| | # Description |
| | Binary Localization prediction is a binary classification task where each input protein *x* is mapped to a label *y* ∈ {0, 1}, corresponding to either "membrane-bound" or "soluble" . |
| |
|
| | **Protein Format:** SA sequence (AF2) |
| |
|
| | # Splits |
| |
|
| | The dataset is from [**DeepLoc: prediction of protein subcellular localization using deep learning**](https://academic.oup.com/bioinformatics/article/33/21/3387/3931857). We employ all proteins (proteins that lack AF2 structures are removed), and split them based on 70% structure similarity (see [ProteinShake](https://github.com/BorgwardtLab/proteinshake/tree/main)), with the number of training, validation and test set shown below: |
| |
|
| | - Train: 6707 |
| | - Valid: 698 |
| | - Test: 807 |
| |
|
| | # Label |
| |
|
| | 0: membrane-bound |
| |
|
| | 1: soluble |
| |
|
