双网GO:通过有效的特征选择来预测蛋白质功能的双网络模型
Zhuoyang Chen1, Qiong Luo1,2
1Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, 511400, China.
Bioinformatics (Oxford, England)
|July 4, 2024
概括
通过从多个蛋白质-蛋白质相互作用网络和属性中选择相关特征,DualNetGO有效地预测蛋白质功能. 这种方法比无区别地结合所有数据的模型提高了准确性,增强了蛋白质功能注释.
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:
- 蛋白质与蛋白质相互作用 (PPI) 网络对于理解蛋白质功能至关重要.
- 不同质的PPI网络在蛋白质功能预测中有效利用信息方面存在挑战.
- 当前的深度学习模型通常将所有网络数据结合起来,可能会增加噪音并降低性能.
研究的目的:
- 开发一个新的双网络模型,DualNetGO,用于准确的蛋白质功能预测.
- 解决有效选择和整合来自各种PPI网络和蛋白质属性的信息的挑战.
- 通过明智地利用来自多个来源的信息来改进现有方法.
主要方法:
- 开发了DualNetGO,一个带有分类器和选择器的双网络架构.
- 来自PPI网络的集成图嵌入,蛋白质域信息和亚细胞定位.
- 在人类和老鼠数据集和CAFA3基准数据上评估模型性能.
主要成果:
- 与其他基于网络的模型相比,DualNetGO在人类和小鼠数据集的基因本体学类别 (BP,MF,CC) 中实现了Fmax得分的显著改善.
- 证明了对CAFA3数据的概括能力和对Esm2嵌入的多功能性.
- 对图形嵌入方法和时间和内存方面的效率表现出不敏感.
结论:
- 从多个PPI网络和蛋白质属性中整合特征的选择性方法优于不分青红白的组合.
- DualNetGO提供了一种强大而高效的蛋白质功能预测方法,其性能优于现有的方法.
- 模型选择最佳特征的能力提高了复杂的生物网络数据的实用性.
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