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

Bioinformatics

Volumn 34, Issue 5, 2018, Pages 828-835

  Zhang, Yijia ^a,b   Zheng, Wei ^a,c   Lin, Hongfei ^a   Wang, Jian ^a   Yang, Zhihao ^a   Dumontier, Michel ^d  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c DALIAN JIAOTONG UNIVERSITY   (China)

^d MAASTRICHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DATA MINING; DRUG INTERACTION; DRUG SURVEILLANCE PROGRAM; HUMAN; PROCEDURES; PUBLICATION;

DATA MINING; DRUG INTERACTIONS; HUMANS; NEURAL NETWORKS (COMPUTER); PHARMACOVIGILANCE; PUBLICATIONS;

EID: 85042940451     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx659     Document Type: Article

References (38)

1. Bengio, Y. et al. (2003) A neural probabilistic language model. J. Mach. Learn. Res., 3, 1137-1155.
2. Bjorne, J. et al. (2013) UTurku: drug named entity recognition and drug-drug interaction extraction using SVM classification and domain knowledge. In: 7th International Workshop on Semantic Evaluation, Atlanta, Georgia, USA, pp. 651-659.
3. Bottiger, Y. et al. (2009) SFINX-A drug-drug interaction database designed for clinical decision support systems. Eur. J. Clin. Pharmacol., 65, 627-633.
4. Cho, K. et al. (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv preprint arXiv: 1406.1078.
5. Chowdhury, M.F.M., and Lavelli, A. (2013) FBK-irst: A multi-phase kernel based approach for drug-drug interaction detection and classification that exploits linguistic information. In: 7th InternationalWorkshop on Semantic Evaluation, Atlanta, Georgia, USA, pp. 351-355.
6. Gartner, T. et al. (2003) On graph kernels: hardness results and efficient alternatives. In: Scholkopf, B. and Warmuth, M.K. (eds.) Learning Theory and Kernel Machines. Springer, Berlin, pp. 129-143.
7. Hachad, H. et al. (2010) A useful tool for drug interaction evaluation: the University of Washington Metabolism and Transport Drug Interaction Database. Hum. Genomics, 5, 61.
8. Hendrickx, I. et al. (2009) Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Association for Computational Linguistics, pp. 94-99.
9. Herrero-Zazo, M. et al. (2013) The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions. J. Biomed. Informatics, 46, 914-920.
10. Hochreiter, S., and Schmidhuber, J. (1997) Long short-term memory. Neural Comput., 9, 1735-1780.
11. Kim, S. et al. (2015) Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach. J. Biomed. Informatics, 55, 23-30.
12. Knox, C. et al. (2011) DrugBank 3.0: A comprehensive resource for 'omics' research on drugs. Nucleic Acids Res., 39, D1035-D1041.
13. Liu, S. et al. (2016) Drug-drug interaction extraction via convolutional neural networks. Comput. Math. Methods Med.
14. Liu, Y. et al. (2015) A dependency-based neural network for relation classification, arXiv preprint arXiv: 1507.04646.
15. Mikolov, T. et al. (2013) Efficient estimation of word representations in vector space, arXiv preprint arXiv: 1301.3781.
16. Mingguang Xiao, C.L. (2016) Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention. In: Proceeding of COLING 2016, the 26th International Conference on Computational Linguistics. Osaka, Japan, pp. 1254-1263.
17. Miranda, V. et al. (2011) Adverse drug reactions and drug interactions as causes of hospital admission in oncology. J. Pain Symptom Manage., 42, 342-353.
18. Miwa, M., and Bansal, M. (2016) End-to-end relation extraction using lstms on sequences and tree structures, arXiv preprint arXiv: 1601.00770.
19. Palangi, H. et al. (2016) Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP), 24, 694-707.
20. Pennington, J. et al. (2014) Glove: Global Vectors for Word Representation. In: Conference on Empirical Methods in Natural Language Processing. Doha, Qatar, pp. 1532-1543.
21. Percha, B., and Altman, R.B. (2013) Informatics confronts drug-drug interactions. Trends Pharmacol. Sci., 34, 178-184.
22. Quan, C. et al. (2016) Multichannel convolutional neural network for biological relation extraction. BioMed Res. Int.
23. Raihani, A., and Laachfoubi, N. (2016) Extracting drug-drug interactions from biomedical text using a feature-based kernel approach. J. Theor. Appl. Inf. Technol., 92, 109.
24. Sahu, S.K., and Anand, A. (2017) Drug-drug interaction extraction from biomedical text using long short term memory network, arXiv preprint. arXiv: 1701.08303.
25. Segura-Bedmar, I. et al. (2014) Lessons learnt from the DDIExtraction-2013 shared task. J. Biomed. Informatics, 51, 152-164.
26. Srivastava, N. et al. (2014) Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 15, 1929-1958.
27. Thomas, P. et al. (2013) WBI-DDI: drug-drug interaction extraction using majority voting. In: 7th International Workshop on Semantic Evaluation, Atlanta, Georgia, USA. pp. 628-635.
28. Thorn, C.F. et al. (2013) PharmGKB: the pharmacogenomics knowledge base. Methods Mol. Biol., 1015, 311-320.
29. Vu, N.T. et al. (2016) Combining recurrent and convolutional neural networks for relation classification, arXiv preprint. arXiv: 1605.07333.
30. Wang, L. et al. (2016) Relation classification via multi-level attention cnns. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany, pp. 1298-1307.
31. Xu, Y. et al. (2015) Classifying relations via long short term memory networks along shortest dependency paths. In: Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal, pp. 1785-1794.
32. Yi, Z. et al. (2017) Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers, arXiv preprint. arXiv: 1705.03261.
33. Yin, W. et al. (2017) Comparative Study of CNN and RNN for Natural Language Processing, arXiv preprint. arXiv: 1702.01923.
34. Zeng, D. et al. (2014) Relation classification via convolutional deep neural network. In: International Conference on Computational Linguistics. Dublin, Ireland, pp. 2335-2344.
35. Zhang, Y. et al. (2012) A single kernel-based approach to extract drug-drug interactions from biomedical literature. PLoS One, 7, e48901.
36. Zhao, Z. et al. (2016) Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics, 32, 3444-3453.
37. Zheng, W. et al. (2016) A graph kernel based on context vectors for extracting drug-drug interactions. J. Biomed. Informatics, 61, 34-43.
38. Zou, W.Y. et al. (2013) Bilingual Word Embeddings for Phrase-Based Machine Translation. In: Conference on Empirical Methods in Natural Language Processing. Seattle, Washington, USA, pp. 1393-1398.