Extracting chemical-protein relations with ensembles of SVM and deep learning models

Database (Oxford). 2018 Jan 1;2018:bay073. doi: 10.1093/database/bay073.


Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network. Their output is combined using majority voting or stacking for final predictions. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the highest performance in the task during the 2017 challenge.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-5/.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural

MeSH terms

  • Data Curation
  • Databases, Chemical*
  • Databases, Protein
  • Machine Learning*
  • Models, Theoretical*
  • Neural Networks, Computer
  • Proteins / chemistry*
  • Reproducibility of Results
  • Support Vector Machine*


  • Proteins