Proteins Combined Score Prediction Based on Improved Gene Expression Programming Algorithm and Protein–Protein Interaction Network Characterization

Predicting the combined score in protein–protein interaction (PPI) networks represents a critical research focus in bioinformatics, as it contributes to enhancing the accuracy of PPI data and uncovering the inherent complexity of biological systems. However, existing intelligent algorithms encounter significant challenges in effectively integrating heterogeneous data sources, capturing the nonlinear dependencies within PPI networks, and improving model generalizability. To address these limitations, this study introduces an enhanced gene expression programming (DF-GEP) algorithm that incorporates dynamic factor optimization. The proposed DF-GEP framework integrates Spearman correlation analysis with kernel ridge regression (SC-KRR) to extract and assign refined weights to key PPI network features. Additionally, the algorithm adaptively regulates selection, crossover, mutation and fitness evaluation processes via dynamic factor adjustment, thereby improving adaptability and predictive precision. Experimental results show that the DF-GEP algorithm consistently outperforms baseline models in both predictive accuracy and stability. Beyond its application to PPI-combined score prediction, the proposed algorithm also exhibits strong potential for addressing complex nonlinear problems in other domains.