Abstract
To solve the problem of ADDI extraction, the construction and implementation of knowledge graph provide a suitable solution for medical knowledge storage and management. This paper designs the construction and implementation of a knowledge graph based on deep learning. Named entity recognition and relationship extraction are carried out on the text of social media data, and then the graph database is used to store medical knowledge and construct the knowledge graph (KG). A DDI prediction method is proposed by combing knowledge graph (KG) and Bi-directional long-short-term memory network (Bi-LSTM) with attention. The multiple DDI sources are integrated by KG, and then are transformed into vectors by the knowledge representation model HolE. Finally, the implicit features of DDI are extracted by Bi-LSTM, and DDI is identified by Softmax classifier. The proposed method effectively combines the advantages of KG, BI-LSTM and attention mechanism, which can not only extract the global information of DDI, but also extract the sequence information of DDI. The experimental results on the DDI corpus dataset validate that the proposed method is effective and feasible for DDI prediction even with the reasonable architecture.
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References
Sandson, N.B., Armstrong, S.C., Cozza, K.L.: An overview of psychotropic Drug-Drug interactions. Psychosomatics 46(5) (2005). https://doi.org/10.1176/appi.psy.46.5.464
Cheng, F., Zhao, Z.: Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inf. Assoc. Jamia e2, 278–286 (2014)
Hina, H., Huma, A., Farya, Z., et al.: Drug-Drug interaction. Profess. Med. J. 24(3) (2017). https://doi.org/10.17957/TPMJ/17.3670
Raihani, A., Laachfoubi, N.: Extracting Drug-Drug interactions from biomedical text using a feature-based kernel approach. J. Theor. Appl. Inf. Technol. 92, 109–120 (2016)
Cami, A., Manzi, S., Arnold, A., et al.: Pharmacointeraction network models predict unknown drug-drug interactions. PLoS ONE 8(4), e61468 (2013)
Bui, Q.C., Sloot, P.M., Mulligen, E.M., et al.: A novel feature-based approach to extract Drug-Drug interactions from biomedical text. Bioinformatics 2014(23), 3365–3371 (2014)
Kim, S., Liu, H., Yeganova, L., et al.: Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach. J. Biomed. Inform. 55, 23–30 (2015)
Jamal, S., Goyal, S., Shanker, A., et al.: Predicting neurological adverse Drug reactions based on biological, chemical and phenotypic properties of drugs using machine learning models. Rep 7(1), 872 (2017). https://doi.org/10.1038/s41598-017-00908-z
Zhu, J., Liu, Y., Wen, C.: MTMA: multi-task multi-attribute learning for the prediction of adverse Drug-Drug interaction. Knowl. Based Syst. 199, 105978 (2020). https://doi.org/10.1016/j.knosys.2020.105978
Zhang, W., Yue, X., Liu, F., et al.: A unified frame of predicting side effects of drugs by using linear neighborhood similarity. BMC Syst. Biol. 11(S6), 101 (2017). https://doi.org/10.1186/s12918-017-0477-2
Rohani, N., Eslahchi, C., Katanforoush, A.: ISCMF: integrated similarity-constrained matrix factorization for Drug–Drug interaction prediction. Network Model. Anal. Health Inf. Bioinf. 9(1), 1–8 (2020). https://doi.org/10.1007/s13721-019-0215-3
Liu, S.Y., Tang, B.Z., Chen, Q.C., et al.: Drug-Drug interaction extraction via convolutional neural networks. Comput. Math. Methods Med. 2016, 6918381 (2016)
Sun, X., Feng, J., Ma, L., et al.: Deep convolution neural networks for Drug-Drug interaction extraction. IEEE Int. Conf. Bioinf. Biomed. (2018). https://doi.org/10.17816/PAVLOVJ2013370-76
Zhao, Z.H., Yang Z.H.L., et al.: Drug-Drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics 32(22), 3444–3453 (2016)
Víctor, S.P., Isabel, S.B.: Evaluation of pooling operations in convolutional architectures for Drug-Drug interaction. BMC Bioinf. 19(Suppl 8), 209 (2018)
Liu, S., Chen, K., Chen, Q., et al.: Dependency-based convolutional neural network for drug-drug interaction extraction. IEEE International Conference on Bioinformatics and Biomedicine, pp. 1074–1080 (2017)
Yi, Z., et al..: Drug-Drug interaction extraction via recurrent neural network with multiple attention layers. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 554–566. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69179-4_39
Shukla, P.K., Shukla, P.K., Sharma, P., et al.: Efficient prediction of Drug-Drug interaction using deep learning models. IET Syst. Biol. 14(4), 211–216 (2020)
Park, C., Park, J., Park, S.: AGCN: attention-based graph convolutional networks for Drug-Drug interaction extraction. Expert Syst. Appl. 159, 113538 (2020)
Abdelaziz, I., Fokoue, A., Hassanzadeh, O., et al.: Large-scale structural and textual similarity-based mining of knowledge graph to predict Drug-Drug interactions. J. Web Seman. 44, 104–117 (2017)
Shen, Y., et al.: KMR: knowledge-oriented medicine representation learning for Drug–Drug interaction and similarity computation. J. Cheminform. 11(1), 1–16 (2019). https://doi.org/10.1186/s13321-019-0342-y
Karim, M.R., Cochez, M., Jares, J.B., et al.: Drug-Drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network. ACM-BCB 2019, pp. 113–123. Niagara Falls, USA (2019)
Lin, X., Quan, Z., Wang, Z.J., et al.: KGNN: knowledge graph neural network for Drug-Drug interaction prediction. In: 29th International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence (2020). https://doi.org/10.24963/ijcai.2020/376
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. Computer Science (2015). arXiv:1510.04935, https://arxiv.org/pdf/1510.04935.pdf
Wu, H., Xing, Y., Ge, W., et al.: Drug-drug interaction extraction via hybrid neural networks on biomedical literature. J. Biomed. Inform. 106, 103432 (2020)
Li, X., Zhang, W., Ding, Q.: Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism. Sig. Process. 161, 136–154 (2019)
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This work is supported by the National Natural Science Foundation of China (Nos. 62172338 and 62072378).
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Zhang, S., Yu, C., Xu, C. (2022). Integrating Knowledge Graph and Bi-LSTM for Drug-Drug Interaction Predication. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_62
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