Update README.md

README.md

@@ -5,7 +5,17 @@ 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}.
 
-The digital label means:
+# 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
+
+# Label
 
 0: Nucleus
 
@@ -26,30 +36,3 @@ The digital label means:
 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
-- **label:** classification label of the sequence
-
-**1:**
-
-**···**