an attention mechanism-based deep neural network for protein lysine lactylation site prediction in Rice (Oryza sativa)
DeepKla provides a deep-learning method for lactylation site prediction. It is implemented by deep learning library Keras and Tensorflow backend. At present, DeepKla only provides prediction of rice lactylation sites; however, it also provides customized model training that enables users to train other PTM prediction models by using their own training data sets.
Installation Download DeepKla by git clone https://github.com/linDing-group/DeepKla
Installation has been tested in Mac OS X with Python 2.7.
Since the package is written in python 2.7, python 2.7 with the pip tool must be installed first. DeepKla uses the following dependencies: numpy, h5py, keras version=2.0.6
You can install these packages first, by the following commands:
pip install numpy
pip install h5py
pip install -v keras==2.0.6
pip install tensorflow
Predict on your own data : cd to the predict folder which contains load_model.py, run:
python load_model.py <your_test_file>.fa <predict_result>.txt
Example:
python load_model.py ../data/fungiForTest.fa fungiForTest_predict_result.txt fungiForTest.pkl
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For advanced users who want to perform training by using their own data:
For training:
python ../SEL_CNN_BiGRU_Attention.py (Computational time: 6453.411189s)
Note: You need to change fastafile, modelfile, pklfile, and model_save's name on your own.