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BioMed Research International
Volume 2016, Article ID 1850404, 10 pages
http://dx.doi.org/10.1155/2016/1850404
Research Article

Multichannel Convolutional Neural Network for Biological Relation Extraction

1Graduate School of System Informatics, Kobe University, Kobe, Japan
2Department of Computer and Information Science, Hefei University of Technology, Hefei, China

Received 22 June 2016; Accepted 9 November 2016

Academic Editor: Oliver Ray

Copyright © 2016 Chanqin Quan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • Ika Novita Dewi, Shoubin Dong, and Jinlong Hu, “Drug-drug interaction relation extraction with deep convolutional neural networks,” 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1795–1802, . View at Publisher · View at Google Scholar
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  • Yijia Zhang, “Letter to the Editor( Response from author): MeSH qualifiers, publication types and relation occurrence frequency are also useful for a better sentence-level extraction of biomedical relations,” Journal of Biomedical Informatics, 2018. View at Publisher · View at Google Scholar
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  • Deyu Zhou, Lei Miao, and Yulan He, “Position-aware deep multi-task learning for drug–drug interaction extraction,” Artificial Intelligence in Medicine, 2018. View at Publisher · View at Google Scholar
  • Yijia Zhang, Hongfei Lin, Zhihao Yang, Jian Wang, Shaowu Zhang, Yuanyuan, Sun, and Liang Yang, “A Hybrid Model Based on Neural Networks for Biomedical Relation Extraction,” Journal of Biomedical Informatics, 2018. View at Publisher · View at Google Scholar
  • Daisuke Nagasato, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Masumoto, Hiroki Enno, Naofumi Ishitobi, Tomoaki Sonobe, Masahiro Kameoka, Masanori Niki, Ken Hayashi, and Yoshinori Mitamura, “Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy,” Journal of Ophthalmology, vol. 2018, pp. 1–6, 2018. View at Publisher · View at Google Scholar
  • Jaewoo Kang, Sangrak Lim, and Kyubum Lee, “Drug drug interaction extraction from the literature using a recursive neural network,” PLoS ONE, vol. 13, no. 1, 2018. View at Publisher · View at Google Scholar