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+test/data.mdb filter=lfs diff=lfs merge=lfs -text
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README.md

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 ---
 license: mit
 ---
+# 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}.
+
+The digital label means:
+
+0: Nucleus
+
+1: Cytoplasm
+
+2: Extracellular
+
+3: Mitochondrion
+
+4: Cell.membrane
+
+5: Endoplasmic.reticulum
+
+6: Plastid
+
+7: Golgi.apparatus
+
+8: Lysosome/Vacuole
+
+9: Peroxisome
+
+# 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: 10414
+- Valid: 1368
+- Test:  1368
+
+# Data format
+
+We organize all data in LMDB format. The architecture of the databse is like:
+
+**length:** The number of samples
+
+**0:** 
+
+- **name:** The UniProt ID of the protein
+
+- **seq:** The structure-aware sequence
+- **plddt**: pLDDT values at all positions
+- **label:** classification label of the sequence
+
+**1:**
+
+**···**	

test/data.mdb

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