Integration of evolutionary computation and ensemble learning in bioinformatics (Case Study: Protein-Peptide...

The performance of classifiers can decline due to irrelevant or non-informative features. Therefore, extracting meaningful high-level features from raw data through effective feature selection and construction is vital. This challenge is particularly significant in bioinformatics, especially for predicting protein-peptide interactions. To address this, we propose IntPPPred, a computational method designed to enhance residue-level prediction of such interactions. IntPPPred leverages evolutionary computation and ensemble learning to build high-level features from selected informative ones, reducing computational complexity and feature space. It first identifies unique and effective features before applying multiple feature constructions using a gravitational search algorithm. This enhances the prediction capability of a stacking-based ensemble classifier, particularly on imbalanced datasets. Experimental results show that IntPPPred improves Matthews correlation coefficient (MCC), F-measure, and precision by at least 1.2%, 19.6%, and 3.9%, respectively, over existing methods. When evaluated on a second dataset, further gains of 5.2%, 24%, 16.7%, and 1.1% were observed in precision, sensitivity, F-measure, and MCC. Additionally, performance consistency between cross-validation and independent test sets confirms the method's robustness. Overall, IntPPPred serves as an effective computational tool that supports experimental research and enhances machine learning performance in protein-peptide interaction prediction.