This is a repository of codes of lncLocator 2.0, an end-to-end lncRNA subcellular localization predictor. You can use this program and know more about it through our website.
The environment on our computer is as follows:
- Python 3.6.8
- PyTorch 1.3.1
- Pandas 1.0.1
- NumPy 1.18.1
- scikit-learn 0.22.1
Run the main.py
to train and test lncLocator 2:
python main.py
And you can configure the training yourself. For example, change the directory of dataset by:
python main.py --train_dataset=path/to/your/train/dataset --dev_dataset=path/to/your/dev/dataset --test_dataset=path/to/your/test/dataset
Check the config.py
to see what you could adjust.
Training log will be recorded in train.log
at the root directory of the project. The curves of loss/auroc/accuracy versus epoch will be drawn and saved at the root directory of the project, titled loss.png
, auroc.png
and acc.png
respectively. The prediction result for test set in every epoch will be saved at the root directory of the project, titled record.csv
.