Jiang Qiangrong, Ma Jiajia
1 Beijing University of Technology, Department of Computer Science, Beijing, P. R. China.
J Bioinform Comput Biol. 2018 Dec;16(6):1850026. doi: 10.1142/S0219720018500269. Epub 2018 Oct 30.
Considering the classification of compounds as a nonlinear problem, the use of kernel methods is a good choice. Graph kernels provide a nice framework combining machine learning methods with graph theory, whereas the essence of graph kernels is to compare the substructures of two graphs, how to extract the substructures is a question. In this paper, we propose a novel graph kernel based on matrix named the local block kernel, which can compare the similarity of partial substructures that contain any number of vertexes. The paper finally tests the efficacy of this novel graph kernel in comparison with a number of published mainstream methods and results with two datasets: NCI1 and NCI109 for the convenience of comparison.
考虑到化合物分类是一个非线性问题，使用核方法是一个不错的选择。图核提供了一个将机器学习方法与图论相结合的良好框架，而图核的本质是比较两个图的子结构，如何提取子结构是一个问题。在本文中，我们提出了一种基于矩阵的新型图核，称为局部块核，它可以比较包含任意数量顶点的部分子结构的相似性。为了便于比较，本文最后使用两个数据集NCI1和NCI109测试了这种新型图核与一些已发表的主流方法相比的有效性。
