##DeepMal
DeepMal:Accurate prediction of protein malonylation sites by deep neural networks
##Pipeline
###DeepMal uses the following dependencies:
- MATLAB2014a
- python 3.6
- numpy
- scipy
- scikit-learn (Deep learning library)
- keras(Machine learning library)
###Guiding principles:
**The data contains training dataset and testing dataset. Training dataset includes ecoli_train,H_train and mus_train Testing dataset includes ecoli_test,H_test and mus_test
**Feature extraction: EAAC.py is the implementation of enhanced amino acid composition. EGAAC.py is the implementation of enhanced grouped amino acid composition. KNN.py is the implementation of K nearest neighbors. DDE.py is the implementation of dipeptide deviation from expected mean. BLOSUM62.py is the implementation of BLOSUM62 matrix.
** Classifier: DL.py is the implementation of DL. DL_1.py is the implementation of DL_1. DNN.py is the implementation of Deep neural network. GRU.py is the implementation of Recurrent neural network. XGBoost_classifier.py is the implementation of XGBoost. SVM_classifier.py is the implementation of SVM.