metadata

license: cc-by-sa-3.0

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:

import pandas as pd
from sklearn.model_selection import train_test_split

train = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv')
train = train.loc[:,['Sequence','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']]
train = train.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1)

train, val = train_test_split(train)
val = val.reset_index(drop=True)

test = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/hpa_testset.csv')
test = test.loc[:,['fasta','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome']].rename(columns={'fasta':'Sequence'})
test = test.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1).sample(frac=1).reset_index(drop=True)

train.to_parquet('data/deeploc/deeploc-train.parquet', index=False)
val.to_parquet('data/deeploc/deploc-val.parquet', index=False)
test.to_parquet('data/deeploc/deeploc-test.parquet', index=False)

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 following datasets:

UniProt

The UniProt Consortium
UniProt: the Universal Protein Knowledgebase in 2023
Nucleic Acids Res. 51:D523–D531 (2023)

The Human Protein Atlas

Mathias Uhlén et al.,Tissue-based map of the human proteome.Science347,1260419(2015).DOI:10.1126/science.1260419