蛋白质结合得分预测基于改进的基因表达编程算法和蛋白质-蛋白质相互作用网络的特性
Sicong Huo1, Pengying Deng1, Jie Zhou2
1School of Information Engineering, Nanning University, Nanning, China.
IET systems biology
|June 16, 2025
概括
这项研究引入了一种新的动态因子基因表达编程 (DF-GEP) 算法,以准确预测蛋白质-蛋白质相互作用得分. 增强的DF-GEP模型提高了生物信息学中的预测准确性和稳定性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 网络分析对于理解生物系统至关重要.
- 现有的方法在异质数据整合,非线性依赖性和可概括性方面扎.
- 准确的PPI得分预测提高了数据可靠性和生物洞察力.
研究的目的:
- 开发一种先进的算法,用于预测蛋白质-蛋白质相互作用网络中的综合得分.
- 克服当前智能算法在数据集成和非线性依赖性捕获方面的局限性.
- 提高PPI预测模型的准确性,稳定性和通用性.
主要方法:
- 引入了一个动态因子基因表达编程 (DF-GEP) 算法.
- 集成的斯皮尔曼相关性分析与内核回归 (SC-KRR) 用于特征加权.
- 采用动态因素调整来优化进化过程 (选择,交叉,突变,适应性评估).
主要成果:
- 与基线模型相比,DF-GEP算法显示出更高的性能.
- 在预测准确性和模型稳定性方面取得了持续的改进.
- 验证了算法在预测PPI综合分数方面的有效性.
结论:
- 拟议的DF-GEP算法在PPI网络分析方面取得了重大进展.
- 该方法有效地处理复杂的非线性问题和异质数据.
- DF-GEP显示出在计算生物学及其他领域更广泛应用的巨大潜力.
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