Chemical-induced disease relation extraction via attention-based distant supervision

Jinghang Gu, Fuqing Sun, Longhua Qian, Guodong Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


Background: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedical entities from scientific literature, its success, however, heavily depends on large-scale biomedical corpora manually annotated with intensive labor and tremendous investment. Results: We present an attention-based distant supervision paradigm for the BioCreative-V CDR extraction task. Training examples at both intra- and inter-sentence levels are generated automatically from the Comparative Toxicogenomics Database (CTD) without any human intervention. An attention-based neural network and a stacked auto-encoder network are applied respectively to induce learning models and extract relations at both levels. After merging the results of both levels, the document-level CDRs can be finally extracted. It achieves the precision/recall/F1-score of 60.3%/73.8%/66.4%, outperforming the state-of-the-art supervised learning systems without using any annotated corpus. Conclusion: Our experiments demonstrate that distant supervision is promising for extracting chemical disease relations from biomedical literature, and capturing both local and global attention features simultaneously is effective in attention-based distantly supervised learning.

Original languageEnglish
Article number403
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 22 Jul 2019
Externally publishedYes


  • Attention
  • Biomedical relation extraction
  • Deep learning
  • Distant supervision

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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