Drug-drug interaction extraction from biomedical texts using long short-term memory network

Journal of Biomedical Informatics

Volumn 86, Issue , 2018, Pages 15-24

  Sahu, Sunil Kumar ^a   Anand, Ashish ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI   (India)

Author keywords

Attention model; DDI extraction; Long short term memory network

Indexed keywords

BRAIN; EXTRACTION; LONG SHORT-TERM MEMORY;

ATTENTION MODEL; DRUG-DRUG INTERACTIONS; FEATURE ENGINEERINGS; IDENTIFICATION OF UNKNOWNS; IMPLICIT FEATURES; NEURAL NETWORK MODEL; SHORT TERM MEMORY; THERAPEUTIC OUTCOMES;

DRUG INTERACTIONS;

ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; BIOMEDICINE; DRUG INTERACTION; INFORMATION PROCESSING; KERNEL METHOD; LONG SHORT TERM MEMORY NETWORK; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; ALGORITHM; KNOWLEDGE BASE; LANGUAGE; LINGUISTICS; MEDICAL INFORMATICS; POLYPHARMACY; PROCEDURES; REPRODUCIBILITY; SEMANTICS; SOFTWARE; STATISTICAL MODEL;

ALGORITHMS; DRUG INTERACTIONS; KNOWLEDGE BASES; LANGUAGE; LINEAR MODELS; LINGUISTICS; MEDICAL INFORMATICS; NEURAL NETWORKS (COMPUTER); POLYPHARMACY; REPRODUCIBILITY OF RESULTS; SEMANTICS; SOFTWARE; SUPPORT VECTOR MACHINE;

EID: 85052543403     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2018.08.005     Document Type: Article

References (47)

1. Hutson, S., Researchers sound alarm on ‘silent’ drug interactions. Nat. Med., 17(1), 2011, 6.
2. I. Segura Bedmar, P. Martinez, D. Sánchez Cisneros, The 1st DDIExtraction-2011 challenge task: Extraction of Drug-Drug Interactions from biomedical texts, in: Proceedings of the 1st Challenge Task on Drug-Drug Interaction Extraction 2011, SEPLN 2011 Workshop, Spain, 2011.
3. I. Segura-Bedmar, P. Martínez, M. Herrero Zazo, SemEval-2013 Task 9: Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013), in: Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), Association for Computational Linguistics, Atlanta, Georgia, USA, 2013, pp. 341–350.
4. Przybyła, P., Shardlow, M., Aubin, S., Bossy, R., de Castilho, R.E., Piperidis, S., McNaught, J., Ananiadou, S., Text mining resources for the life sciences. Database, 2016, 2016, baw145.
5. Singhal, A., Leaman, R., Catlett, N., Lemberger, T., McEntyre, J., Polson, S., Xenarios, I., Arighi, C., Lu, Z., Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges. Database, 2016, 2016, baw161.
6. Bobic, T., Fluck, J., Hofmann-Apitius, M., SCAI: extracting drug-drug interactions using a rich feature vector. Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, Association for Computational Linguistics, Atlanta, Georgia, USA, 675–683.
7. Hailu, N., Hunter, L.E., Cohen, K.B., UColorado_SOM: extraction of drug-drug interactions from biomedical text using knowledge-rich and knowledge-poor features. Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, Association for Computational Linguistics, Atlanta, Georgia, USA, 684–688.
8. Thomas, P., Neves, M., Rocktäschel, T., Leser, U., WBI-DDI: drug-drug interaction extraction using majority voting. Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, Association for Computational Linguistics, Atlanta, Georgia, USA, 628–635.
9. Rastegar-Mojarad, M., Boyce, R.D., Prasad, R., UWM-TRIADS: classifying drug-drug interactions with two-stage SVM and post-processing. Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, Association for Computational Linguistics, Atlanta, Georgia, USA, 667–674.
10. Björne, J., Kaewphan, S., Salakoski, T., UTurku: drug named entity recognition and drug-drug interaction extraction using SVM classification and domain knowledge. Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, Association for Computational Linguistics, Atlanta, Georgia, USA, 651–659.
11. Chowdhury, M.F.M., Lavelli, A., FBK-irst: a multi-phase kernel based approach for Drug-Drug interaction detection and classification that exploits linguistic information. Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, Association for Computational Linguistics, Atlanta, Georgia, USA, 351–355.
12. Bokharaeian, B., Diaz, A., NIL_UCM: extracting Drug-Drug interactions from text through combination of sequence and tree kernels. Second Joint Conference on Lexical and Computational Semantics (∗SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, Association for Computational Linguistics, Atlanta, Georgia, USA, 644–650.
13. Kim, S., Liu, H., Yeganova, L., Wilbur, W.J., Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach. J. Biomed. Inform. 55 (2015), 23–30.
14. Gurulingappa, H., Toldo, L., Rajput, A.M., Kors, J.A., Taweel, A., Tayrouz, Y., Automatic detection of adverse events to predict drug label changes using text and data mining techniques. Pharmacoepidemiol. Drug Saf. 22:11 (2013), 1189–1194.
15. Gurulingappa, H., Mateen-Rajpu, A., Toldo, L., Extraction of potential adverse drug events from medical case reports. J. Biomed. Semant., 3(1), 2012, 1.
16. Harpaz, R., Callahan, A., Tamang, S., Low, Y., Odgers, D., Finlayson, S., Jung, K., LePendu, P., Shah, N.H., Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf. 37:10 (2014), 777–790.
17. Xu, R., Wang, Q., Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles. J. Biomed. Inform. 55 (2015), 64–72.
18. Yang, M., Kiang, M., Shang, W., Filtering big data from social media-building an early warning system for adverse drug reactions. J. Biomed. Inform. 54 (2015), 230–240.
19. Qian, L., Zhou, G., Tree kernel-based protein-protein interaction extraction from biomedical literature. J. Biomed. Inform. 45:3 (2012), 535–543.
20. Bravo, À., Piñero, J., Queralt-Rosinach, N., Rautschka, M., Furlong, L.I., Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinform., 16(1), 2015, 1.
21. Rink, B., Harabagiu, S., Roberts, K., Automatic extraction of relations between medical concepts in clinical texts. J. Am. Med. Inform. Assoc. 18:5 (2011), 594–600.
22. Liu, S., Tang, B., Chen, Q., Wang, X., Drug-drug interaction extraction via convolutional neural networks. Comput. Math. Methods Med., 2016.
23. Zhao, Z., Yang, Z., Luo, L., Lin, H., Wang, J., Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics, 2016, btw486.
24. Zhang, Y., Zheng, W., Lin, H., Wang, J., Yang, Z., Dumontier, M., Drug-drug interaction extraction via hierarchical rnns on sequence and shortest dependency paths. Bioinformatics 34:5 (2017), 828–835.
25. Wang, W., Yang, X., Yang, C., Guo, X., Zhang, X., Wu, C., Dependency-based long short term memory network for drug-drug interaction extraction. BMC Bioinform., 18(16), 2017, 578.
26. Asada, M., Miwa, M., Sasaki, Y., Extracting Drug-Drug Interactions with Attention CNNs. BioNLP 2017, 2017, Association for Computational Linguistics, Vancouver, Canada, 9–18.
27. Hochreiter, S., Schmidhuber, J., Long short-term memory. Neural Comput. 9:8 (1997), 1735–1780.
28. A. Graves, Generating sequences with recurrent neural networks, arXiv preprint arXiv: 1308.0850.
29. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P., Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12 (2011), 2493–2537.
30. S.K. Sahu, A. Anand, K. Oruganty, N. Gattu, Relation extraction from clinical texts using domain invariant convolutional neural network, in: Proceedings of the 15th Workshop on Biomedical Natural Language Processing, 2016, pp. 206–215.
31. T. Mikolov, M. Karafiát, L. Burget, J. Cernocký S. Khudanpur, Recurrent neural network based language model, in: INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, Makuhari, Chiba, Japan, September 26–30, 2010, 2010, pp. 1045–1048.
32. R. Pascanu, T. Mikolov, Y. Bengio, Understanding the exploding gradient problem, CoRR abs/1211.5063.
33. D. Bahdanau, K. Cho, Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate, CoRR abs/1409.0473.
34. Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, E. Hovy, Hierarchical Attention Networks for Document Classification, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, San Diego, California, 2016, pp. 1480–1489.
35. Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., Xu, B., Attention-based bidirectional long short-term memory networks for relation classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2016, Association for Computational Linguistics, Berlin, Germany, 207–212.
36. D. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv: 1412.6980.
37. Herrero-Zazo, M., Segura-Bedmar, I., Martínez, P., Declerck, T., The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions. J. Biomed. Inform. 46:5 (2013), 914–920.
38. Pennington, J., Socher, R., Manning, C., Glove: global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, Association for Computational Linguistics, Doha, Qatar, 1532–1543.
39. TH, M., Sahu, S., Anand, A., Evaluating distributed word representations for capturing semantics of biomedical concepts. Proceedings of BioNLP 15, 2015, Association for Computational Linguistics, Beijing, China, 158–163.
40. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15:1 (2014), 1929–1958.
41. M.F.M. Chowdhury, A. Lavelli, Exploiting the Scope of Negations and Heterogeneous Features for Relation Extraction: A Case Study for Drug-Drug Interaction Extraction., in: HLT-NAACL, 2013, pp. 765–771.
42. V. Suárez-Paniagua, I. Segura-Bedmar, Extraction of Drug-Drug Interactions by Recursive Matrix-Vector Spaces, in: 6thInternational Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), 2016, p. 65.
43. R. Socher, C.C.-Y. Lin., C.D. Manning, A.Y. Ng, Parsing Natural Scenes and Natural Language with Recursive Neural Networks, in: ICML, 2011.
44. Zheng, W., Lin, H., Luo, L., Zhao, Z., Li, Z., Zhang, Y., Yang, Z., Wang, J., An attention-based effective neural model for drug-drug interactions extraction. BMC Bioinform., 18(1), 2017, 445.
45. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C., Neural architectures for named entity recognition. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016, Association for Computational Linguistics, San Diego, California, 260–270.
46. Kavuluru, R., Rios, A., Tran, T., Extracting drug-drug interactions with word and character-level recurrent neural networks. 2017 IEEE International Conference on Healthcare Informatics (ICHI), 2017, IEEE, 5–12.
47. Lim, S., Lee, K., Kang, J., Drug drug interaction extraction from the literature using a recursive neural network. PLOS ONE 13:1 (2018), 1–17.