标签指导的多尺度图神经网络蛋白质作用关系预测方法

Label guided multi-scale graph neural network for protein-protein interactions prediction

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%。

A protein-protein interactions prediction method based on label guided multi-scale graph neural network is proposed,which not only enhances the representation ability of data,but also introduces label information to guide learning.Firstly,the multi-scale graph representation is obtained by graph data augmentation,and the multi-scale graph representation is input into graph neural network to obtain multi-scale protein representation,and comparative learning is introduced to further improve the protein characterization ability.Secondly,the self-learning label relation graph is constructed to learn the relationship between labels and obtain the feature representation of labels.Finally,the prediction of protein-protein interactions is guided by the feature representation of labels.Experiments are carried out on three public datasets.Compared with the optimal benchmark method,the proposed method has better performance.Specifically,compared with the best baseline method,the micro-F,scores on the three datasets SHS27k,SHS148k and STRING increase by 2.01%,0.94% and 0.93% respectively.