PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK
DOI:
https://doi.org/10.29244/ijsa.v4i1.328Keywords:
binary logistic regression, drug-drug interaction, random forest, support vector machinesAbstract
One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.
Downloads
References
Bajusz, D., Rácz, A., & Héberger, K. (2015). Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? Journal of Cheminformatics, 7(1): 20.
Breiman, L. (2001). Random forests. Machine Learning, 45(1): 5–32.
Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J., & Bork, P. (2008). Drug target identification using side-effect similarity. Science, 321(5886): 263–266.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3): 273–297.
Fadhilah, A. R., & Notobroto, H. B. (2016). Analisis regresi logistik biner pada kejadian transient ischemic attack (TIA) di RSUD Dr. Soetomo Surabaya. Jurnal Biometrika Dan Kependudukan, 5(2): 157–165.
Fradgley, S. (2003). Interaksi Obat dalam Farmasi Klinis (Clinical Pharmacy) Menuju Pengobatan Rasional dan Penghargaan Pilihan Pasien (Aslam M, Tan CK, Prayitno A, Ed). Jakarta (ID): PT Elex Media Komputindo Kelompok Gramedia.
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).
Huang, J., Niu, C., Green, C. D., Yang, L., Mei, H., & Han, J.-D. J. (2013). Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network. PLoS Computational Biology, 9(3): e1002998. https://doi.org/10.1371/journal.pcbi.1002998
Lingga P, R. D., Fatichah, C., & Purwitasari, D. (2017). Deteksi gempa berdasarkan data twitter menggunakan decision tree, random forest, dan SVM. Jurnal Teknik ITS, 6(1): A159–A162.
Marino, M., Tirta, I. M., & Dewi, Y. S. (2014). Perbandingan analisis diskriminan linier, diskriminan linier robust dan regresi logistik biner. Studi kasus pada penjurusan bidang IPA / IPS siswa tingkat SMA Negeri 1 Bangorejo Banyuwangi. Prosiding Seminar Nasional Matematika, 192–200. Jember (ID): Universitas Jember.
Rachman, F., & Purnami, S. W. (2012). Perbandingan klasifikasi tingkat keganasan breast cancer dengan menggunakan regresi logistik ordinal dan support vector machine (SVM). Jurnal Sains Dan Seni ITS, 1(1): D130–D135.
Winata, H. M., Afendi, F. M., & Fitrianto, A. (2019). Peningkatan akurasi klasifikasi interaksi farmakodinamik obat berbasis seleksi pasangan obat takberinteraksi. Indonesian Journal of Statistics and Its Applications, 3(3): 247–259.
