Issue No. 04 - July-Aug. (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.50
Zhihao Yang , Coll. of Comput. Sci., Dalian Univ. of Technol., Dalian, China
Hongfei Lin , Coll. of Comput. Sci., Dalian Univ. of Technol., Dalian, China
Yijia Zhang , Coll. of Comput. Sci., Dalian Univ. of Technol., Dalian, China
Jian Wang , Coll. of Comput. Sci., Dalian Univ. of Technol., Dalian, China
Yanpeng Li , Coll. of Comput. Sci., Dalian Univ. of Technol., Dalian, China
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.
proteins, data mining, graph theory, medical information systems, PPI corpora, hash subgraph pairwise kernel, protein-protein interaction extraction, biomedical literature, biomedical text mining, graph kernel, dependency graphs, sentence structure, Kernel, Syntactics, Proteins, Protein engineering, Feature extraction, Bioinformatics, Arrays, graph kernel., Biomedical text mining, hash, interaction extraction
Zhihao Yang, Hongfei Lin, Yijia Zhang, Jian Wang and Yanpeng Li, "Hash Subgraph Pairwise Kernel for Protein-Protein Interaction Extraction," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1190-1202, 2012.