DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA.  Our proposed method, named as DNA-binding Protein Prediction model using Chou's general PseAAC (DPP-PseAAC), demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset.  DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively.  The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding.  A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/.
DPP-PseAAC
After feature extraction, we have employed Random Forest (RF) model to rank the features. 
Random Forest (RF)
Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. 
Recursive Feature Elimination (RFE)
Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. 
Support Vector Machine (SVM)