참고문헌
- Takeda, T., Hao, M., Cheng, T., Bryant, S. H., & Wang, Y. (2017). Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. Journal of cheminformatics, 9(1), 16. https://doi.org/10.1186/s13321-017-0200-8
- Wang, M., Chen, Y., Qian, B., Liu, J., Wang, S., Long, G., & Wang, F. (2017). Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding. arXiv preprint arXiv:1712.08875.
- Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., . . . Sayeeda, Z. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research, 46(D1), D1074-D1082. https://doi.org/10.1093/nar/gkx1037
- Lamurias, A., Sousa, D., Clarke, L. A., & Couto, F. M. (2019). BOLSTM: classifying relations via long short-term memory networks along biomedical ontologies. BMC bioinformatics, 20(1), 1-12. https://doi.org/10.1186/s12859-018-2565-8
- Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W., & Liu, S. (2020). A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics.
- Lamurias, A., & Couto, F. M. (2019). Text mining for bioinformatics using biomedical literature. Encyclopedia of bioinformatics and computational biology, 1, 602-611. https://doi.org/10.1016/B978-0-12-809633-8.20409-3
- Jan, B., Farman, H., Khan, M., Imran, M., Islam, I. U., Ahmad, A., Jeon, G. (2019). Deep learning in big data Analytics: A comparative study. Computers & Electrical Engineering, 75, 275-287. https://doi.org/10.1016/j.compeleceng.2017.12.009
- Liu, S., Chen, K., Chen, Q., & Tang, B. (2016). Dependency-based convolutional neural network for drug-drug interaction extraction. Paper presented at the 2016 IEEE international conference on bioinformatics and biomedicine (BIBM).
- Yi, Z., Li, S., Yu, J., Tan, Y., Wu, Q., Yuan, H., & Wang, T. (2017). Drug-drug interaction extraction via recurrent neural network with multiple attention layers. Paper presented at the International Conference on Advanced Data Mining and Applications.
- Wang, G.-G., Deb, S., Gandomi, A. H., & Alavi, A. H. (2016). Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing, 177, 147-157. https://doi.org/10.1016/j.neucom.2015.11.018
- Ferdousi,R, Safda,i, R, Omidi, Y. (2017) Computational prediction of drug-drug interactions drugs functional similarities, Journal of Biomedical Informatics,V.70, 54- 64. https://doi.org/10.1016/j.jbi.2017.04.021
- Sahu, S. K., & Anand, A. (2018). Drug-drug interaction extraction from biomedical texts using long short-term memory network. Journal of biomedical informatics, 86, 15-24. https://doi.org/10.1016/j.jbi.2018.08.005
- Guo, Y., Dai, X., Jermsittiparsert, K., & Razmjooy, N. (2020). An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 6, 885-894.
- Rohani, N., Eslahchi, C., & Katanforoush, A. (2020). ISCMF: Integrated similarity-constrained matrix factorization for drug-drug interaction prediction. Network Modeling Analysis in Health Informatics and Bioinformatics, 9(1), 1-8. https://doi.org/10.1007/s13721-019-0207-3
- Park, C., Park, J., & Park, S. (2020). AGCN: Attention-based Graph Convolutional Networks for Drug-Drug Interaction Extraction. Expert Systems with Applications, 113538. https://doi.org/10.1016/j.eswa.2020.113538
- Xu, B., Shi, X., Zhao, Z., & Zheng, W. (2018). Leveraging biomedical resources in bi-lstm for drug-drug interaction extraction. IEEE Access, 6, 33432-33439. https://doi.org/10.1109/ACCESS.2018.2845840
- Agrawal, Pandit, & Dubey (2016), Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization, International Journal of Intelligent Systems and Applications, , PP.34-50.
- Rezaul Karim, M., Cochez, M., Bosco Jares, J., Uddin, M., Beyan, O., & Decker, S. (2019). Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network. arXiv preprint arXiv:1908.01288.
- Abdel-Basset, M., Wang, G.-G., Sangaiah, A. K., & Rushdy, E. (2019). Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimedia Tools and Applications, 78(4), 3861-3884. https://doi.org/10.1007/s11042-017-4803-x
- Li, L., Sun, L., Xue, Y., Li, S., Huang, X. and Mansour, R.F., 2021. Fuzzy Multilevel Image Thresholding Based on Improved Coyote Optimization Algorithm. IEEE Access, 9, pp.33595-33607. https://doi.org/10.1109/ACCESS.2021.3060749
- Shen, Y., Yuan, K., Yang, M., Tang, B., Li, Y., Du, N., & Lei, K. (2019). KMR: knowledge-oriented medicine representation learning for drug-drug interaction and similarity computation. Journal of cheminformatics, 11(1), 22. https://doi.org/10.1186/s13321-019-0342-y
- Yoshida, K., Zhao, P., Zhang, L., Abernethy, D. R., Rekic, D., Reynolds, K. S., . . . Huang, S.-M. (2017). In Vitro-In Vivo Extrapolation of Metabolism-and Transporter-Mediated Drug-Drug Interactions-Overview of Basic Prediction Methods. Journal of pharmaceutical sciences, 106(9), 2209-2213. https://doi.org/10.1016/j.xphs.2017.04.045
- Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., & Li, X. (2017). Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC bioinformatics, 18(1), 18. https://doi.org/10.1186/s12859-016-1415-9
- Zhang, W., Jing, K., Huang, F., Chen, Y., Li, B., Li, J., & Gong, J. (2019). SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug-drug interactions. Information Sciences, 497, 189-201. https://doi.org/10.1016/j.ins.2019.05.017
- Zhou, D., Miao, L., & He, Y. (2018). Position-aware deep multi-task learning for drug-drug interaction extraction. Artificial intelligence in medicine, 87, 1-8. https://doi.org/10.1016/j.artmed.2018.03.001
- Lee, Park & Ahn, (2019), Novel deep learning model for more accurate prediction of drug-drug Interaction effects, BMC Bioinformatics, https://doi.org/10.1186/s12859-019-3013-0.
- Gottlieb, A., Stein, G. Y., Oron, Y., Ruppin, E., & Sharan, R. (2012). INDI: a computational framework for inferring drug interactions and their associated recommendations. Molecular systems biology, 8(1).
- Shtar, Rokach & Shapira (2019), Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures, PLoS ONE 14(8): e0219796. https://doi.org/10.1371/journal.pone.0219796.
- Abualigah, L. M., Khader, A. T., Hanandeh, E. S., & Gandomi, A. H. (2017). A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Applied Soft Computing, 60, 423-435. https://doi.org/10.1016/j.asoc.2017.06.059
- Bolaji, A. L. a., Al-Betar, M. A., Awadallah, M. A., Khader, A. T., & Abualigah, L. M. (2016). A comprehensive review: Krill Herd algorithm (KH) and its applications. Applied Soft Computing, 49, 437-446. https://doi.org/10.1016/j.asoc.2016.08.041
- Feng, Y., Wang, G.-G., Deb, S., Lu, M., & Zhao, X.-J. (2017). Solving 0-1 knapsack problem by a novel binary monarch butterfly optimization. Neural Computing and Applications, 28(7), 1619-1634. https://doi.org/10.1007/s00521-015-2135-1
- Wang, G.-G., Deb, S., Gao, X.-Z., & Coelho, L. D. S. (2016). A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. International Journal of Bio-Inspired Computation, 8(6), 394-409. https://doi.org/10.1504/IJBIC.2016.081335
- Wang, G.-G., Gandomi, A. H., Alavi, A. H., & Gong, D. (2019). A comprehensive review of krill herd algorithm: variants, hybrids and applications. Artificial Intelligence Review, 51(1), 119-148. https://doi.org/10.1007/s10462-017-9559-1
- Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
- Rohani, N., & Eslahchi, C. (2019). Drug-Drug interaction predicting by neural network Using integrated Similarity. Scientific reports, 9(1), 1-11. https://doi.org/10.1038/s41598-018-37186-2