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.