Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016 (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.

Linked References

  1. C. Quan, M. Wang, and F. Ren, “An unsupervised text mining method for relation extraction from biomedical literature,” PLoS ONE, vol. 9, no. 7, Article ID e102039, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. L. Li, R. Guo, Z. Jiang, and D. Huang, “An approach to improve kernel-based Protein-Protein Interaction extraction by learning from large-scale network data,” Methods, vol. 83, pp. 44–50, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Knox, V. Law, T. Jewison et al., “DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs,” Nucleic Acids Research, vol. 39, supplement 1, pp. D1035–D1041, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. V. Law, C. Knox, Y. Djoumbou et al., “DrugBank 4.0: shedding new light on drug metabolism,” Nucleic Acids Research, vol. 42, no. 1, pp. D1091–D1097, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Zanzoni, L. Montecchi-Palazzi, M. Quondam, G. Ausiello, M. Helmer-Citterich, and G. Cesareni, “Mint: a molecular interaction database,” FEBS Letters, vol. 513, no. 1, pp. 135–140, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Kerrien, B. Aranda, L. Breuza et al., “The IntAct molecular interaction database in 2012,” Nucleic Acids Research, vol. 40, pp. D841–D846, 2012. View at Publisher · View at Google Scholar
  7. R. Bunescu, R. Mooney, A. Ramani, and E. Marcotte, “Integrating co-occurrence statistics with information extraction for robust retrieval of protein interactions from medline,” in Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology (BioNLP '06), New York, NY, USA, 2006.
  8. K. Fundel, R. Küffner, and R. Zimmer, “RelEx—relation extraction using dependency parse trees,” Bioinformatics, vol. 23, no. 3, pp. 365–371, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. I. Segura-Bedmar, P. Martínez, and C. de Pablo-Sánchez, “A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents,” BMC Bioinformatics, vol. 12, supplement 2, p. S1, 2011. View at Publisher · View at Google Scholar
  10. B. Cui, H. Lin, and Z. Yang, “SVM-based protein-protein interaction extraction from medline abstracts,” in Proceedings of the 2nd International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA '07), pp. 182–185, IEEE, Zhengzhou, China, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Erkan, A. Özgür, and D. R. Radev, “Semi-supervised classification for extracting protein interaction sentences using dependency parsing,” in Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL '07), vol. 7, pp. 228–237, June 2007. View at Scopus
  12. C. Sun, L. Lin, and X. Wang, “Using maximum entropy model to extract protein-protein interaction information from biomedical literature,” in Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues: Third International Conference on Intelligent Computing, ICIC 2007 Qingdao, China, August 21–24, 2007 Proceedings, vol. 4681 of Lecture Notes in Computer Science, pp. 730–737, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  13. I. Segura-Bedmar, P. Martínez, and C. de Pablo-Sánchez, “Using a shallow linguistic kernel for drug-drug interaction extraction,” Journal of Biomedical Informatics, vol. 44, no. 5, pp. 789–804, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Kim, H. Liu, L. Yeganova, and W. J. Wilbur, “Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach,” Journal of Biomedical Informatics, vol. 55, pp. 23–30, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Arora and A. Rangarajan, “A compositional approach to language modeling,” https://arxiv.org/abs/1604.00100.
  16. Y. Bengio, H. Schwenk, J.-S. Senécal, F. Morin, and J. L. Gauvain, “Neural probabilistic language models,” in Innovations in Machine Learning, Studies in Fuzziness and Soft Computing, pp. 137–186, Springer, Berlin, Germany, 2006. View at Google Scholar
  17. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” https://arxiv.org/abs/1301.3781.
  18. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 (NIPS 2012), NIPS Proceedings, pp. 1097–1105, Neural Information Processing Systems Foundation, 2012. View at Google Scholar
  19. A. Mnih and G. Hinton, “Three new graphical models for statistical language modelling,” in Proceedings of the 24th International Conference on Machine Learning (ICML '07), pp. 641–648, ACM, Corvallis, Ore, USA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Pennington, R. Socher, and C. D. Manning, “Global vectors for word representation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '14), vol. 14, pp. 1532–1543, Doha, Qatar, October 2014.
  21. S. Pyysalo, F. Ginter, F. Moen, and T. Salakoski, “Distributional semantics resources for biomedical text processing,” in Proceedings of the Languages in Biology and Medicine (LBM '13), pp. 39–44, Tokyo, Japan, December 2013.
  22. S.-P. Choi and S.-H. Myaeng, “Simplicity is better: revisiting single kernel ppi extraction,” in Proceedings of the 23rd International Conference on Computational Linguistics (Coling '10), pp. 206–214, Association for Computational Linguistics, Beijing, China, August 2010.
  23. Z. Yang, N. Tang, X. Zhang, H. Lin, Y. Li, and Z. Yang, “Multiple kernel learning in protein-protein interaction extraction from biomedical literature,” Artificial Intelligence in Medicine, vol. 51, no. 3, pp. 163–173, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Miwa, R. Sætre, Y. Miyao, and J. Tsujii, “Protein-protein interaction extraction by leveraging multiple kernels and parsers,” International Journal of Medical Informatics, vol. 78, no. 12, pp. e39–e46, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Miwa, R. Sætre, Y. Miyao, and J. Tsujii, “A rich feature vector for protein-protein interaction extraction from multiple corpora,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '09), pp. 121–130, Association for Computational Linguistics, August 2009. View at Scopus
  26. R. Collobert, R. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” The Journal of Machine Learning Research, vol. 12, no. 2-1, pp. 2493–2537, 2011. View at Google Scholar
  27. M. D. Zeiler, “ADADELTA: an adaptive learning rate method,” https://arxiv.org/abs/1212.5701.
  28. I. Segura-bedmar, P. Martínez, and M. Herrero-zazo, “2013 SemEval-2013 task 9: extraction of drug-drug interactions from biomedical texts (ddiextraction 2013),” in Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval '13), Association for Computational Linguistics, Atlanta, Ga, USA, June 2013.
  29. Md. F. M. Chowdhury and A. Lavelli, “FBK-irst: a multi-phase kernel based approach for drug-drug interaction detection and classification that exploits linguistic information,” in Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: 7nth International Workshop on Semantic Evaluation (SemEval '13), pp. 351–355, Atlanta, Ga, USA, June 2013.
  30. P. Thomas, M. Neves, T. Rocktäschel, and U. Leser, “WBI-DDI: drug-drug interaction extraction using majority voting,” in Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval '13), vol. volume 2, pp. 628–635, Atlanta, Ga, USA, June 2013.
  31. J. Björne, S. Kaewphan, and T. Salakoski, “Uturku: drug named entity recognition and drug-drug interaction extraction using svm classification and domain knowledge,” in Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics (SemEval '13), vol. 2, pp. 651–659, 2013.
  32. Z. Zhao, Z. Yang, L. Luo, H. Lin, and J. Wang, “Drug drug interaction extraction from biomedical literature using syntax convolutional neural network,” Bioinformatics, vol. 32, no. 22, pp. 3444–3453, 2016. View at Publisher · View at Google Scholar
  33. R. Bunescu, R. Ge, R. J. Kate et al., “Comparative experiments on learning information extractors for proteins and their interactions,” Artificial Intelligence in Medicine, vol. 33, no. 2, pp. 139–155, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Pyysalo, F. Ginter, J. Heimonen et al., “BioInfer: a corpus for information extraction in the biomedical domain,” BMC Bioinformatics, vol. 8, no. 1, article 50, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. Y. Zhang and B. Wallace, “A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification,” https://arxiv.org/abs/1510.03820.
  36. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103, ACM, July 2008. View at Scopus