Huo Sicong, Deng Pengying, Zhou Jie, Lu Tao, Li Qingnian, Wang Xiaowei
School of Information Engineering, Nanning University, Nanning, China.
School of Artificial Intelligence, Guangxi Vocational and Technical College, Nanning, China.
IET Syst Biol. 2025 Jan-Dec;19(1):e70024. doi: 10.1049/syb2.70024.
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.
预测蛋白质-蛋白质相互作用（PPI）网络中的综合得分是生物信息学的一个关键研究重点，因为它有助于提高PPI数据的准确性并揭示生物系统的内在复杂性。然而，现有的智能算法在有效整合异构数据源、捕捉PPI网络中的非线性依赖关系以及提高模型泛化能力方面面临重大挑战。为了解决这些局限性，本研究引入了一种结合动态因子优化的增强基因表达式编程（DF-GEP）算法。所提出的DF-GEP框架将斯皮尔曼相关性分析与核岭回归（SC-KRR）相结合，以提取关键PPI网络特征并为其分配精确权重。此外，该算法通过动态因子调整自适应地调节选择、交叉、变异和适应度评估过程，从而提高适应性和预测精度。实验结果表明，DF-GEP算法在预测准确性和稳定性方面始终优于基线模型。除了应用于PPI综合得分预测外，所提出的算法在解决其他领域的复杂非线性问题方面也具有强大的潜力。
