APA-Net

APA-Net is a deep learning model designed for learning context specific APA usage. This guide covers the steps necessary to set up and run APA-Net.

Installation

Before running APA-Net, ensure you have Python installed on your system. Clone this repository to your local machine:

git clone https://github.com/BaderLab/APA-Net.git
cd APA-Net

pip install .

Usage

To train the APA-Net model, use the train_script.py script with the necessary command-line arguments:

python train_script.py \
--train_data "/path/to/train_data.npy" \
--train_seq "/path/to/train_seq.npy" \
--valid_data "/path/to/valid_data.npy" \
--valid_seq "/path/to/valid_seq.npy" \
--profiles "/path/to/celltype_profiles.tsv" \
--modelfile "/path/to/model_output.pt" \
--batch_size 64 \
--epochs 200 \
--project_name "APA-Net_Training" \
--device "cuda:1" \
--use_wandb "True"

Arguments

• --train_data: Path to the training data file.
• --train_seq: Path to the training sequence data file.
• --valid_data: Path to the validation data file.
• --valid_seq: Path to the validation sequence data file.
• --profiles: Path to the cell type profiles file.
• --modelfile: Path where the trained model will be saved.
• --batch_size: Batch size for training (default: 64).
• --epochs: Number of training epochs (default: 200).
• --project_name: Name of the project for wandb logging.
• --device: Device to run the training on (e.g., 'cuda:1').
• --use_wandb: Flag to enable or disable wandb logging ('True' or 'False').