A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types

Nur Ramadhan - Institut Teknologi Telkom Purwokerto, Indonesia,
Azka Khoirunnisa - Telkom University, Bandung, Indonesia
Kurnianingsih Kurnianingsih - Politeknik Negeri Semarang, Indonesia
Takako Hashimoto - Chiba University of Commerce, Japan

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.7.3.1171


Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.


Drug; random oversampling; support vector machine; balancing data

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Li, Zhe, Yongtai Zhang, and Nianping Feng. "Mesoporous silica nanoparticles: Synthesis, classification, drug loading, pharmacokinetics, biocompatibility, and application in drug delivery." Expert opinion on drug delivery, vol. 16, no. 3, pp. 219-237, 2019, doi: 10.1080/17425247.2019.1575806.

Nascimento, André CA, Ricardo BC Prudêncio, and Ivan G. Costa. "A multiple kernel learning algorithm for drug-target interaction prediction." BMC bioinformatics, vol. 17, no. 1, pp. 1-16, 2016, doi: 10.1186/s12859-016-0890-3.

Yasuo, Nobuaki, Yusuke Nakashima, and Masakazu Sekijima. "Code-dti: Collaborative deep learning-based drug-target interaction prediction." 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018, pp. 792-797, doi: 10.1109/BIBM.2018.8621368.

Olayan, Rawan S., Haitham Ashoor, and Vladimir B. Bajic. "DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches." Bioinformatics, vol. 34, no. 7, pp. 1164-1173, 2018, doi: 10.1093/bioinformatics/btx731.

Ryu, Jae Yong, Hyun Uk Kim, and Sang Yup Lee. "Deep learning improves prediction of drug–drug and drug–food interactions." Proceedings of the National Academy of Sciences, vol. 115, no. 18, pp. E4304-E4311, 2018, doi: 10.1073/pnas.180329411.

Shtar, Guy, Lior Rokach, and Bracha Shapira. "Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures." PloS one, vol. 14, no. 8, pp. e0219796, 2019, doi: 10.1371/journal.pone.0219796..

Peska, Ladislav, Krisztian Buza, and Júlia Koller. "Drug-target interaction prediction: a Bayesian ranking approach." Computer methods and programs in biomedicine, vol. 15, no. 2, pp. 15-21, 2017, doi: 10.1016/j.cmpb.2017.09.003.

Ezzat, Ali, et al. "Drug-target interaction prediction via class imbalance-aware ensemble learning." BMC bioinformatics, vol. 17, no. 19, pp. 267-276, 2016, doi: 10.1186/s12859-016-1377-y.

Bagherian, Maryam, et al. "Machine learning approaches and databases for prediction of drug–target interaction: a survey paper." Briefings in bioinformatics, vol. 22, no. 1, pp. 247-269, 2021, doi: 10.1093/bib/bbz157.

Chen, Ruolan, et al. "Machine learning for drug-target interaction prediction." Molecules, vol. 23, no. 9, pp. 2208, 2018, doi: 10.3390/molecules23092208.

Lavecchia, Antonio. "Machine-learning approaches in drug discovery: methods and applications." Drug discovery today, vol. 20, no. 3, pp. 318-331, 2015, doi: 10.1016/j.drudis.2014.10.012.

Liu, Yong, et al. "Neighborhood regularized logistic matrix factorization for drug-target interaction prediction." PLoS computational biology, vol. 12, no. 2, pp. e1004760, 2016, doi: 10.1371/journal.pcbi.1004760.

Peón, Antonio, Stefan Naulaerts, and Pedro J. Ballester. "Predicting the reliability of drug-target interaction predictions with maximum coverage of target space." Scientific reports 7.1, 1-11, Jun. 2017, doi: 10.1038/s41598-017-04264-w.

He, Tong, et al. "SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines." Journal of cheminformatics, vol. 9, no. 1, pp. 1-14, 2017, doi: 10.1186/s13321-017-0209-z.

Mohammed Nazim Uddin, Md. Ferdous Bin Hafiz, Sohrab Hossain and Shah Mohammad Mominul Islam, “Drug Sentiment Analysis using Machine Learning Classifiers” International Journal of Advanced Computer Science and Applications(IJACSA), vol. 13, no. 1, 2022, doi: 10.14569/IJACSA.2022.0130112.

Wongyikul, Pakpoom, et al. "High alert drugs screening using gradient boosting classifier." Scientific Reports, vol. 11, no. 1, pp. 1-24, Oct. 2021, doi: 10.1038/s41598-021-99505-4.

Shanbhag, Shrinivas V., et al. "Drug-Drug Interaction Extraction Based on Deep Learning Models." Soft Computing for Problem Solving. Springer, Singapore, pp. 691-706, 2021, doi: 10.1007/978-981-16-2709-5_53.

Liu, Bin, et al. "Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery." Applied Intelligence 52.4, 3705-3727, 2022, doi: 10.1007/s10489-021-02495-z.

Thafar, Maha A., et al. "DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques." Journal of Cheminformatics 12.1, 1-17. 2020. doi: 10.1186/s13321-020-00447-2.

Pliakos, Konstantinos, and Celine Vens. "Drug-target interaction prediction with tree-ensemble learning and output space reconstruction." BMC bioinformatics, vol. 21, no. 1, pp. 1-11. 2020, doi: 10.1186/s12859-020-3379-z.

Mohan, Maya. "Ensemble Learning Models for Drug Target Interaction Prediction." 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 16 June, 2022, doi: 10.1109/ICAAIC53929.2022.9793081.

Ye, Qing, Xiaolong Zhang, and Xiaoli Lin. "Drug-target interaction prediction via multiple output deep learning." 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, January, 2020, doi: 10.1109/BIBM49941.2020.9313488.

Mahmud, SM Hasan, et al. "Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting." Analytical biochemistry 589, pp. 113507, 2020, doi: 10.1016/j.ab.2019.113507.

Mahmud, SM Hasan, et al. "iDTi-CSsmoteB: identification of drug–target interaction based on drug chemical structure and protein sequence using XGBoost with over-sampling technique SMOTE." IEEE Access 7, pp. 48699-48714, 2019, doi: 10.1109/ACCESS.2019.2910277.

Branco, Paula, Luís Torgo, and Rita P. Ribeiro. "A survey of predictive modeling on imbalanced domains." ACM Computing Surveys (CSUR) 49.2, pp. 1-50, 2016, doi: 10.1145/2907070.

Nur Ghaniaviyanto Ramadhan, Adiwijaya and Ade Romadhony, “Preprocessing Handling to Enhance Detection of Type 2 Diabetes Mellitus based on Random Forest” International Journal of Advanced Computer Science and Applications(IJACSA), 12(7), 2021, doi: 10.14569/IJACSA.2021.0120726.

Ertekin, Şeyda. "Adaptive oversampling for imbalanced data classification." Information Sciences and Systems 2013. Springer, Cham 264, pp. 261-269, 2013, doi: 10.1007/978-3-319-01604-7_26.

Zhang, Yongli. "Support vector machine classification algorithm and its application." International conference on information computing and applications (ICICA). Springer, Berlin, Heidelberg, 308, May, 2012, doi: 10.1007/978-3-642-34041-3_27.

Ramadhan, Nur Ghaniaviyanto, and Azka Khoirunnisa. "Klasifikasi Data Malaria Menggunakan Metode Support Vector Machine." JURNAL MEDIA INFORMATIKA BUDIDARMA 5.4, pp. 1580-1584, 2021, doi: 10.30865/mib.v5i4.3347.

Ramadhan, Nur Ghaniaviyanto, and Teguh Ikhlas Ramadhan. "Analysis Sentiment based on IMDB aspects from movie reviews using SVM." Sinkron: jurnal dan penelitian teknik informatika 7.1, 39-45, 2022, doi: 10.33395/sinkron.v7i1.11204.


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