Tree-based ensemble learning models for protein-protein interactions detection: a review and experimental evaluation

Experimental evaluations across three benchmark datasets-DIP, HPRD, and STRING-demonstrated that LightGBM consistently achieved the highest performance among all models, with average accuracy reaching up to 86%, and top sensitivity, specificity, and precision scores. XGBoost followed closely, showing strong generalization and robustness due to its regularization capabilities. Gradient Boosting provided competitive accuracy but lagged slightly in computational efficiency. Random Forest, while the most interpretable, showed comparatively lower sensitivity but maintained solid precision, highlighting its strength in minimizing false positives. Overall, LightGBM and XGBoost outperformed other methods in handling large, complex, and imbalanced PPI datasets. These results affirm the suitability of advanced ensemble learning techniques for enhancing PPI prediction and offer practical guidance on selecting models based on performance priorities and application constraints.