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--- |
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license: mit |
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viewer: True |
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--- |
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# Description |
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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 |
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- Valid: 1368 |
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- 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 |
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- **label:** classification label of the sequence |
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**1:** |
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**···** |
