Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods.
Chang et al. REF propose an interaction pattern tree kernel method in which they extract PPIs by integrating the PPI patterns with a convolution tree kernel.
PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
Database: The Journal of Biological Databases and Curation
Database: The Journal of Biological Databases and Curation
Computer Science Medicine