Graph Kernels and Applications in Protein Classification - Jiang Qiangrong; Xiong Zhikang; Zhai Can (Institute of Advanced Engineering and Science, 2014)
Protein classification is a well established research field concerned with the discovery of molecule’s properties through informational techniques. Graph-based kernels provide a nice framework combining machine learning techniques with graph theory. In this paper we introduce a novel graph kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues. In experiments on classification of graph models of proteins, the method based on Weisfeiler-Lehman shortest path kernel with complement graphs outperformed other state-of-art methods.