Ghanbari sorkhi, Ali, Iranpour Mobarakeh, Majid, Hashemi, Seyed Mohammad Reza, Faridpour, Maryam. (1400). Predicting drug-target interaction based on bilateral local models using a decision tree-based hybrid support vector machine. هنرهای کاربردی, 12(2), 135-144. doi: 10.22075/ijnaa.2021.5023
Ali Ghanbari sorkhi; Majid Iranpour Mobarakeh; Seyed Mohammad Reza Hashemi; Maryam Faridpour. "Predicting drug-target interaction based on bilateral local models using a decision tree-based hybrid support vector machine". هنرهای کاربردی, 12, 2, 1400, 135-144. doi: 10.22075/ijnaa.2021.5023
Ghanbari sorkhi, Ali, Iranpour Mobarakeh, Majid, Hashemi, Seyed Mohammad Reza, Faridpour, Maryam. (1400). 'Predicting drug-target interaction based on bilateral local models using a decision tree-based hybrid support vector machine', هنرهای کاربردی, 12(2), pp. 135-144. doi: 10.22075/ijnaa.2021.5023
Ghanbari sorkhi, Ali, Iranpour Mobarakeh, Majid, Hashemi, Seyed Mohammad Reza, Faridpour, Maryam. Predicting drug-target interaction based on bilateral local models using a decision tree-based hybrid support vector machine. هنرهای کاربردی, 1400; 12(2): 135-144. doi: 10.22075/ijnaa.2021.5023

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
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
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