LPTK: A linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task

Neha Warikoo, Yung Chun Chang, Wen Lian Hsu

研究成果: 雜誌貢獻文章同行評審

12 引文 斯高帕斯(Scopus)

摘要

Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical-Disease Relation corpus and Protein-Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.

原文英語
期刊Database
2018
發行號2018
DOIs
出版狀態已發佈 - 1月 1 2018

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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