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README.md
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---
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license: cc-by-sa-3.0
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---
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# DeepLoc-2.0 Training Data
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Dataset from https://services.healthtech.dtu.dk/services/DeepLoc-2.0/ used to train the DeepLoc-2.0 model.
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## Data preparation
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Data downloaded and processed using the following Python script:
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```python
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import pandas as pd
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from sklearn.model_selection import train_test_split
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train = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv')
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train = train.loc[:,['Sequence','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']]
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train = train.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1)
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train, val = train_test_split(train)
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val = val.reset_index(drop=True)
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test = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/hpa_testset.csv')
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test = test.loc[:,['fasta','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome']].rename(columns={'fasta':'Sequence'})
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test = test.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1).sample(frac=1).reset_index(drop=True)
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train.to_parquet('data/deeploc/deeploc-train.parquet', index=False)
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val.to_parquet('data/deeploc/deploc-val.parquet', index=False)
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test.to_parquet('data/deeploc/deeploc-test.parquet', index=False)
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```
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## Citation
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**DeepLoc-2.0:**
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```
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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
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```
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The DeepLoc data is a derivative of the following datasets:
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**UniProt**
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```
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The UniProt Consortium
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UniProt: the Universal Protein Knowledgebase in 2023
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Nucleic Acids Res. 51:D523–D531 (2023)
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```
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**The Human Protein Atlas**
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```
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Mathias Uhlén et al.,Tissue-based map of the human proteome.Science347,1260419(2015).DOI:10.1126/science.1260419
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```
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