PIPE: a protein-protein interaction passage extraction module for BioCreative challenge

Yung Chun Chang, Chun Han Chu, Yu Chen Su, Chien Chin Chen, Wen Lian Hsu

研究成果: 雜誌貢獻文章同行評審

20 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號baw101
期刊Database : the journal of biological databases and curation
2016
DOIs
出版狀態已發佈 - 1月 1 2016
對外發佈

ASJC Scopus subject areas

  • 資訊系統
  • 生物化學、遺傳與分子生物學 (全部)
  • 農業與生物科學 (全部)

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