This project is intended to generate a model for Crispr-Cas9 targeting efficiency prediction.
Below is the layout of the whole model.
This model includes four components:
- embedding layer
- convolutional neural network and recurrent neural network layer
- fully connected layer
- input perturbation layer.
- keras
- tensorflow
- h2o
- sklearn
- pandas
- numpy
change the model directory in config.py when old model need to be loaded for testing or transfer learning
transfer_learning = True
loaded_model_path = os.path.join(cur_dir, "dataset/best_model/<cellline>_lstm_model.h5")
When no model needs to be loaded, change to
transfer_learning = False
Test old models only, change "training" in config.py
training = False
Training new models
training = True
Make sure the data is in dataset/<cellline>/ folder and execute
./run.sh dataset/<cellline>