---
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