|
--- |
|
license: cc-by-4.0 |
|
--- |
|
|
|
# DeepLoc-2.0 Training Data |
|
|
|
Dataset from https://services.healthtech.dtu.dk/services/DeepLoc-2.0/ used to train the DeepLoc-2.0 model. |
|
|
|
## Data preparation |
|
Data downloaded and processed using the following Python script: |
|
|
|
```python |
|
import pandas as pd |
|
|
|
df = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv').drop(['Unnamed: 0', 'Partition'], axis=1) |
|
df['labels'] = df[['Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']].astype('float32').values.tolist() |
|
df['Membrane'] = df['Membrane'].astype('float32') |
|
df = df[['Kingdom', 'ACC', 'Sequence','Membrane','labels']] |
|
|
|
train = df.sample(frac=0.8) |
|
df = df.drop(train.index) |
|
val = df.sample(frac=0.5) |
|
test = df.drop(val.index) |
|
|
|
train = train.reset_index(drop=True) |
|
val = val.reset_index(drop=True) |
|
test = test.reset_index(drop=True) |
|
|
|
train.to_parquet('deeploc-train.parquet', index=False) |
|
val.to_parquet('deploc-val.parquet', index=False) |
|
test.to_parquet('deeploc-test.parquet', index=False) |
|
``` |
|
|
|
## Labels |
|
|
|
{'Cell membrane': 0, |
|
'Cytoplasm': 1, |
|
'Endoplasmic reticulum': 2, |
|
'Extracellular': 3, |
|
'Golgi apparatus': 4, |
|
'Lysosome/Vacuole': 5, |
|
'Mitochondrion': 6, |
|
'Nucleus': 7, |
|
'Peroxisome': 8, |
|
'Plastid': 9} |
|
|
|
## Citation |
|
|
|
**DeepLoc-2.0:** |
|
|
|
``` |
|
Vineet Thumuluri and others, DeepLoc 2.0: multi-label subcellular localization prediction using protein language models, Nucleic Acids Research, Volume 50, Issue W1, 5 July 2022, Pages W228–W234, https://doi.org/10.1093/nar/gkac278 |
|
``` |
|
|
|
The DeepLoc data is a derivative of the UniProt dataset: |
|
|
|
**UniProt** |
|
|
|
``` |
|
The UniProt Consortium |
|
UniProt: the Universal Protein Knowledgebase in 2023 |
|
Nucleic Acids Res. 51:D523–D531 (2023) |
|
``` |
|
|
