DPP-PseAAC: A DNA-binding Protein Prediction model using Chou's general PseAAC.

•We have presented DPP-PseAAC, a new computational model to identify DNA-binding proteins in an efficient and accurate way.•Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information.•We have employed Random Forest (RF) model to rank and identify the most important features.•We have further 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.•DPP-PseAAC demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset.