GNNGL-PPI:使用基于全球图和局部子图的图形神经网络对蛋白质-蛋白质相互作用的多类预测
Xin Zeng1, Fan-Fang Meng1, Meng-Liang Wen2
1College of Mathematics and Computer Science, Dali University, 671003, Dali, China.
BMC genomics
|May 9, 2024
概括
本研究介绍了GNNGL-PPI,这是一种用于预测蛋白质与蛋白质相互作用 (PPI) 的新型图形神经网络方法. GNNGL-PPI提高了多类PPI预测的准确性,为传统实验方法提供了更有效的替代方案.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 网络科学 网络科学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 是生物过程,疾病机制和治疗发展的基础.
- 准确识别PPI类别和提高计算预测准确度是重大挑战.
- 现有的PPI预测计算方法经常与多类别分类和不平衡的数据集扎.
研究的目的:
- 开发一种新的计算方法,用于精确预测蛋白质与蛋白质相互作用的多类别.
- 解决处理复杂的PPI数据和不平衡的类分布的现有方法的局限性.
- 为了提高生物和临床应用中预测蛋白质-蛋白质相互作用的效率和可靠性.
主要方法:
- 拟议的GNNGL-PPI是一种图形神经网络方法,使用图形同态网络 (GIN) 进行全球图形特征和GIN As Kernel (GIN-AK) 进行本地子图形特征.
- 整合了不对称损失 (ASL) 功能,以管理不同PPI类别的不平衡样本分布.
- 在使用随机,BFS和DFS分区算法对六个基准数据集进行了GNNGL-PPI评估.
主要成果:
- 与最先进的多类型PPI预测方法相比,GNNGL-PPI表现优越,F1测量证明了这一点.
- 该方法在识别特定类别的蛋白质-蛋白质相互作用方面取得了高准确性.
- 解释性分析证实了GNNGL-PPI在预测蛋白质与蛋白质相互作用方面的可靠性和有效性.
结论:
- GNNGL-PPI为多类蛋白质-蛋白质相互作用预测提供了强大而准确的解决方案.
- 全球和本地图形特征的整合,以及ASL,显著提高了预测能力.
- 这种计算方法通过改进PPI识别,为推动生物研究和药物发现提供了有价值的工具.
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