《山东大学学报(理学版)》 ›› 2023, Vol. 58 ›› Issue (12): 22-30.doi: 10.6040/j.issn.1671-9352.1.2022.8766
王新生(
),朱小飞*(),李程鸿
Xinsheng WANG(),Xiaofei ZHU*(),Chenghong LI
摘要：
提出了一种标签指导的多尺度图神经网络蛋白质作用关系(label guided multi-scale graph neural network protein-protein interactions, LGMG-PPI)预测方法, 不仅增强了数据的表征能力, 还引入了标签信息指导学习。首先, 通过图数据增强得到多尺度图表示, 并将多尺度图表示输入图神经网络得到多尺度蛋白质表示, 再引入对比学习进一步提高蛋白质表征能力; 其次, 构造自学习的标签关系图, 学习标签之间的关系, 得到标签的特征表示; 最后, 通过标签的特征表示, 对蛋白质作用关系的预测进行指导。在3个公开的数据集上进行了实验, 与最优基准方法相比, LGMG-PPI方法具有更好的性能, 相比最优基准方法, 在SHS27k、SHS148k和STRING这3个数据集上的micro-F1分数分别提升了2.01%、0.94%和0.93%。
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