VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models

Shreya Singh, Nguyen-Quoc-Khanh Le, Cheng Wang

Abstract: Highlights•This study introduces Seq-Alignment, a novel feature enhancing accuracy in protein sequence classification models.•Employing gradient boosting regressor in an ensemble approach effectively addresses the classification of virulence factors.•The proposed model namely VF-Pred attains an accuracy of 83.5%, outperforming previous methods by 2.3%.