Hash Subgraph Pairwise Kernel for Protein-Protein Interaction Extraction

Authors:
Yijia Zhang;Hongfei Lin;Zhihao Yang;Jian Wang;Yanpeng Li
Affiliations:
Dalian University of Technology, Dalian;Dalian University of Technology, Dalian;Dalian University of Technology, Dalian;Dalian University of Technology, Dalian;Dalian University of Technology, Dalian
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2012

Citing 12
Cited 1

The Frame-Based Module of the SUISEKI Information Extraction System

IEEE Intelligent Systems
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions

Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Kernel methods for relation extraction

The Journal of Machine Learning Research
BioRAT: extracting biological information from full-length papers

Bioinformatics
A shortest path dependency kernel for relation extraction

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
RelEx---Relation extraction using dependency parse trees

Bioinformatics
Comparative experiments on learning information extractors for proteins and their interactions

Artificial Intelligence in Medicine
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Simplicity is better: revisiting single kernel PPI extraction

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Multiple kernel learning in protein-protein interaction extraction from biomedical literature

Artificial Intelligence in Medicine
Neighborhood hash graph kernel for protein-protein interaction extraction

Journal of Biomedical Informatics
Parsing biomedical literature

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing

High precision rule based PPI extraction and per-pair basis performance evaluation

Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics

Quantified Score

Hi-index	0.00

Visualization

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.