| | --- |
| | license: mit |
| | viewer: True |
| | --- |
| | # 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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| | **···** |
