Graph kernels and applications in protein classification

Jiang Qiangrong*, Xiong Zhikan

Abstract

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.