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| license: mit |
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| # Description |
| Subcellular Localization prediction is a 10-class classification task to predict where a protein locates in the cell, where each input protein *x* is mapped to a label *y* ∈ {0, 1, ..., 9}. |
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| The digital label means: |
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| 0: Nucleus |
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| 1: Cytoplasm |
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| 2: Extracellular |
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| 3: Mitochondrion |
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| 4: Cell.membrane |
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| 5: Endoplasmic.reticulum |
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| 6: Plastid |
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| 7: Golgi.apparatus |
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| 8: Lysosome/Vacuole |
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| 9: Peroxisome |
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| # Splits |
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| **Structure type:** AF2 |
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| 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: |
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| - Train: 10414 |
| - Valid: 1368 |
| - Test: 1368 |
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| # Data format |
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| We organize all data in LMDB format. The architecture of the databse is like: |
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| **length:** The number of samples |
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| **0:** |
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| - **name:** The UniProt ID of the protein |
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| - **seq:** The structure-aware sequence |
| - **label:** classification label of the sequence |
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| **1:** |
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| **···** |
