metadata

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:

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 = df.melt(['Kingdom', 'ACC', 'Sequence','Membrane'], var_name='Location').query('value == 1.0').drop(labels='value', axis=1).astype({'Membrane': 'int8'})

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)

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)