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---
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" .
# Splits
**Structure type:** AF2
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
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