At present, most of the researches on protein classification are based on graph kernels. The essence of graph kernels is to extract the substructure and use the similarity of substructures as the kernel values. In this paper, we propose a novel graph kernel named vertex-edge similarity kernel (VES kernel) based on mixed matrix, the innovation point of which is taking the adjacency matrix of the graph as the sample vector of each vertex and calculating kernel values by finding the most similar vertex pair of two graphs. In addition, we combine the novel kernel with the neural network and the experimental results show that the combination is better than the existing advanced methods.

中文翻译：

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图核结合神经网络进行蛋白质分类

目前，大多数蛋白质分类研究都是基于图核的。图核的本质是提取子结构，利用子结构的相似度作为核值。在本文中，我们提出了一种新的基于混合矩阵的图核，称为顶点-边相似核（VES kernel），其创新点是以图的邻接矩阵作为每个顶点的样本向量，计算核值找到两个图最相似的顶点对。此外，我们将新颖的内核与神经网络相结合，实验结果表明该组合优于现有的先进方法。