Subcellular Localization Prediction
Collection
This group contains all relative training datasets and model checkpoints trained on these datasets for Subcellular Localization Prediction. • 2 items • Updated
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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}.
Splits
Protein Format: SA sequence (AF2)
The dataset is from DeepLoc: prediction of protein subcellular localization using deep learning. We employ all proteins (proteins that lack AF2 structures are removed), and split them based on 70% structure similarity (see ProteinShake), with the number of training, validation and test set shown below:
- Train: 10414
- Valid: 1368
- Test: 1368
Label
0: Nucleus
1: Cytoplasm
2: Extracellular
3: Mitochondrion
4: Cell.membrane
5: Endoplasmic.reticulum
6: Plastid
7: Golgi.apparatus
8: Lysosome/Vacuole
9: Peroxisome
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