Discovering the interactions between proteins mentioned in biomedi-cal literatures is one of the core topics of text mining in the field of life science. In this paper, we propose a system under interaction pattern generation approach to capture frequent PPI patterns in text with the use of official BioC API and Semantic Class Labeling. We also present an interaction pattern tree kernel method that integrates the PPI pattern with convolution tree kernel to extract protein-protein interactions. Empirical evaluations on the LLL, IEPA, and HPRD50 corpora demonstrate that our method is effective and outper-forms several well-known PPI extraction methods. 1 Introduction With the growing number of research papers, researchers now have difficulty in retrieving those that exactly fulfill their needs. As for life scientists, relationships between entities mentioned in these papers are the major target of interest. Among biomed relation types, protein– protein interaction (PPI) extraction is becoming critical in the field of molecular biology due to demands for automatic discovery of molecular pathways and interactions in the literature.
|Number of pages||7|
|Journal||BioCreative V workshop|
|Publication status||Published - 2015|
Chang, Y-C., Su, Y-C., Chu, C. H., Chen, C. C., & Hsu, W. L. (2015). Protein-protein Interaction Passage Extraction Using the Interaction Pattern Kernel Approach for the BioCreative 2015 BioC Track. BioCreative V workshop, 10-16.