پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 715 |
| تعداد مقالات | 10,285 |
| تعداد مشاهده مقاله | 72,171,767 |
| تعداد دریافت فایل اصل مقاله | 63,907,544 |
Predicting drug-target interaction based on bilateral local models using a decision tree-based hybrid support vector machine | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 11، دوره 12، شماره 2، بهمن 2021، صفحه 135-144 اصل مقاله (532.67 K) | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5023 | ||
| نویسندگان | ||
| Ali Ghanbari sorkhi* 1؛ Majid Iranpour Mobarakeh2؛ Seyed Mohammad Reza Hashemi3؛ Maryam Faridpour4 | ||
| 1Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran | ||
| 2Department of Computer Engineering and IT, Payam Noor University, Tehran, Iran | ||
| 3Young Researchers and Elite Club Qazvin Branch Islamic Azad University, Qazvin, Iran | ||
| 4Department of Electrical and Computer Engineering, Mahdishahr Branch, Islamic Azad University, Mahdishahr, Iran | ||
| تاریخ دریافت: 21 خرداد 1399، تاریخ بازنگری: 13 آذر 1400، تاریخ پذیرش: 01 بهمن 1399 | ||
| چکیده | ||
| Identifying the interaction between the drug and the target proteins plays a very important role in the drug discovery process. Because prediction experiments of this process are time consuming, costly and tedious, Computational prediction can be a good way to reduce the search space to examine the interaction between drug and target instead of using costly experiments. In this paper, a new solution based on known drug-target interactions based on bilateral local models is introduced. In this method, a hybrid support vector machine based on the decision tree is used to decide and optimize the two-class classification. Using this machine to manage data related to this application has performed well. The proposed method on four criteria datasets including enzymes (Es), ion channels (IC), G protein coupled receptors (GPCRs) and nuclear receptors (NRs), based on AUC, AUPR, ROC and running time has been evaluated. The results show an improvement in the performance of the proposed method. | ||
| کلیدواژهها | ||
| Drug-target interaction؛ bilateral local model؛ decision tree؛ hybrid SVM | ||
| مراجع | ||
|
[1] F. Cheng, C. Liu, J. Jiang, W. Lu, W. Li, G. Liu, et al., ”Prediction of drug-target interactions and drug repositioning via network-based inference,” PLoS Comput Biol, vol. 8, p. e1002503, 2012. [2] Y. Deng, X. Xu, Y. Qiu, J. Xia, W. Zhang, and S. Liu, ”A multimodal deep learning framework for predicting drug-drug interaction events,” Bioinformatics, 2020. [3] A. Ezzat, M. Wu, X.-L. Li, and C.-K. Kwoh, ”Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey,” Briefings in bioinformatics, vol. 20, pp. 1337-1357, 2019. [4] A. Ezzat, P. Zhao, M. Wu, X.-L. Li, and C.-K. Kwoh, ”Drug-target interaction prediction with graph regularized matrix factorization,” IEEE/ACM transactions on computational biology and bioinformatics, vol. 14, pp. 646-656, 2016. [5] A. Ezzat, M. Wu, X.-L. Li, and C.-K. Kwoh, ”Drug-target interaction prediction via class imbalance-aware ensemble learning,” BMC bioinformatics, vol. 17, pp. 267-276, 2016. [6] S. Fakhraei, B. Huang, L. Raschid, and L. Getoor, ”Network based drug-target interaction prediction with probabilistic soft logic,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, pp. 775-787, 2014. [7] S. G¨unther, M. Kuhn, M. Dunkel, M. Campillos, C. Senger, E. Petsalaki, et al., ”SuperTarget and Matador: resources for exploring drug-target relationships,” Nucleic acids research, vol. 36, pp. D919-D922, 2007. [8] M. Hattori, Y. Okuno, S. Goto, and M. Kanehisa, ”Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways,” Journal of the American Chemical Society, vol. 125, pp. 11853-11865, 2003. [9] Z. He, J. Zhang, X.-H. Shi, L.-L. Hu, X. Kong, Y.-D. Cai, et al., ”Predicting drug-target interaction networks based on functional groups and biological features,” PloS one, vol. 5, p. e9603, 2010. [10] M. Kanehisa, S. Goto, M. Hattori, K. F. Aoki-Kinoshita, M. Itoh, S. Kawashima, et al., ”From genomics to chemical genomics: new developments in KEGG,” Nucleic acids research, vol. 34, pp. D354-D357, 2006. [11] M. J. Keiser, B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin, and B. K. Shoichet, ”Relating protein pharmacology by ligand chemistry,” Nature biotechnology, vol. 25, pp. 197-206, 2007. [12] M. A. Kumar and M. Gopal, ”A hybrid SVM based decision tree,” Pattern Recognition, vol. 43, pp. 3977-3987, 2010. [13] H. Li, Z. Gao, L. Kang, H. Zhang, K. Yang, K. Yu, et al., ”TarFisDock: a web server for identifying drug targets with docking approach,” Nucleic acids research, vol. 34, pp. W219-W224, 2006. [14] A. Mongia and A. Majumdar, ”Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization,” Plos one, vol. 15, p. e0226484, 2020. [15] A. Mongia, V. Jain, E. Chouzenoux, and A. Majumdar, ”Deep Latent Factor Model for Predicting Drug Target Interactions,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 1254-1258. [16] K. Sachdev and M. K. Gupta, ”A comprehensive review of feature based methods for drug target interaction prediction,” Journal of biomedical informatics, vol. 93, p. 103159, 2019. [17] K. Sachdev and M. K. Gupta, ”A hybrid ensemble-based technique for predicting drug–target interactions,” Chemical Biology & Drug Design, 2020. [18] I. Schomburg, A. Chang, C. Ebeling, M. Gremse, C. Heldt, G. Huhn, et al., ”BRENDA, the enzyme database: updates and major new developments,” Nucleic acids research, vol. 32, pp. D431-D433, 2004. [19] T. F. Smith and M. S. Waterman, ”Identification of common molecular subsequences,” Journal of molecular biology, vol. 147, pp. 195-197, 1981. [20] M. A. Thafar, R. S. Olayan, H. Ashoor, S. Albaradei, V. B. Bajic, X. Gao, et al., ”DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques,” Journal of Cheminformatics, vol. 12, pp. 1-17, 2020. [21] T. Van Laarhoven and E. Marchiori, ”Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile,” PloS one, vol. 8, p. e66952, 2013. [22] D. S. Wishart, C. Knox, A. C. Guo, D. Cheng, S. Shrivastava, D. Tzur, et al., ”DrugBank: a knowledgebase for drugs, drug actions and drug targets,” Nucleic acids research, vol. 36, pp. D901-D906, 2008. [23] Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, ”Prediction of drug–target interaction networks from the integration of chemical and genomic spaces,” Bioinformatics, vol. 24, pp. i232-i240, 2008. [24] M. A. Yıldırım, K.-I. Goh, M. E. Cusick, A.-L. Barab´asi, and M. Vidal, ”Drug—target network,” Nature biotechnology, vol. 25, pp. 1119-1126, 2007. [25] H. Yu, J. Chen, X. Xu, Y. Li, H. Zhao, Y. Fang, et al., ”A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data,” PloS one, vol. 7, p. e37608, 2012. [26] X. Zheng, H. Ding, H. Mamitsuka, and S. Zhu, ”Collaborative matrix factorization with multiple similarities for predicting drug-target interactions,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, pp. 1025-1033. | ||
|
آمار تعداد مشاهده مقاله: 16,494 تعداد دریافت فایل اصل مقاله: 9,894 |
||