Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths.

Journal: Bioinformatics (Oxford, England)
Published Date: Mar 1, 2018

Abstract

MOTIVATION: Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks-based methods typically focus on sentence sequence to identify these DDIs, however the shortest dependency path (SDP) between the two entities contains valuable syntactic and semantic information. Effectively exploiting such information may improve DDI extraction.

Authors

  • Yijia Zhang
    School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Wei Zheng
    School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China. zhengwei@jit.edu.cn.
  • Hongfei Lin
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Zhihao Yang
    College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
  • Michel Dumontier
    Stanford University, Stanford, CA USA.

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