title: Hash subgraph pairwise kernel for protein-protein interaction extraction.
abstract: Extracting protein-protein interaction (PPI) from biomedical literature is an important task in biomedical text mining (BioTM). In this paper, we propose a hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the novel kernel is to use the hierarchical hash labels to express the structural information of subgraphs in a linear time. We apply the graph kernel to compute dependency graphs representing the sentence structure for protein-protein interaction extraction task, which can efficiently make use of full graph structural information, and particularly capture the contiguous topological and label information ignored before. We evaluate the proposed approach on five publicly available PPI corpora. The experimental results show that our approach significantly outperforms all-path kernel approach on all five corpora and achieves state-of-the-art performance.
authors.affiliation: Dalian University of Technology, Dalian, China. zhyj@dlut.edu.cn
authors.forename: Yijia
authors.initials: Y
authors.lastname: Zhang
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lin
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Yang
authors.forename: Jian
authors.initials: J
authors.lastname: Wang
authors.forename: Yanpeng
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins