We employed 12 feature encoding schemes that cover nucleic acid composition, position-specific, and physicochemical properties information, whose corresponding optimal feature set identified and developed their respective baseline models using eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. For a given sequence, Stack-ORI predicts its calss and probability values
