Abstract
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.Database URL:
Highlights
Due to the rapidly growing number of research articles, researchers have found it difficult to retrieve the articles of their interest
To extract protein–protein interaction (PPI) from biomedical literatures in an effective manner, we present PIPE, a module named for PPI Pattern Extraction used in the Collaborative Biocurator Assistant Task (BioC) [5] at BioCreative V
We evaluate the performance of these operators to demonstrate the effectiveness of interaction pattern tree (IPT)
Summary
Due to the rapidly growing number of research articles, researchers have found it difficult to retrieve the articles of their interest. The ultimate goal of PPI extraction is to recognize various interactions including. To extract PPIs from biomedical literatures in an effective manner, we present PIPE, a module named for PPI Pattern Extraction used in the Collaborative Biocurator Assistant Task (BioC) [5] at BioCreative V. To develop a PPI passage extraction module, we model PPI extraction as a classification problem and propose an IPT structure to represent syntactic, content and semantic information in text.
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