Pencarian Rongga Berpotensi Binding Site pada Protein dengan Menggunakan Support Vector Machine (SVM)
Abstract
Bioinformatika merupakan ilmu multidisipliner yang melibatkan berbagai bidang ilmu. Salah satu aplikasi dari bioinformatika adalah dalam proses desain obat berbantuan komputer. Dalam desain obat berbantuan komputer salah satu langkah awal yang dibutuhkan adalah mencari suatu rongga pada protein, rongga tersebut nantinya untu melekat suatu ligan(partikel kecil) maupun protein yang merupakan partikel atau protein dari calon obat. Dalam penelitian ini untuk pencarian binding site digunakan metode klasifikasi dengan Support Vector Machine. Hasil dari pencarian binding site dengan metode ini menunjukkan akurasi G-Mean yang cukup tinggi yaitu 0,903 atau 90,3
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Batuwitage, Manohara Rukshan Kannangara, “Enhanced Class Imbalance Learning Methods for Support Vector Machines”, Thesis of Doctor of Philosophy Hilary Term 2010, St. Cross College, 2010.
Bekkar, Mohamed, dan Taklit Akrouf Alitouche, “Imbalanced Data Learning Approaches Review”, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol. 4, No. 4, hal. 15-33, 2013.
H. He, E.A. Garcia, “Learning from imbalanced data”. IEEE Trans. Knowl. Data Eng, Vol. 21, no.9, hal. 1263-1284,2009.
Hendlich, Manfred, Rippmann, Friedrich dan Gerhard Barnickel, “LIGSITE: Automatic and efficient detection of potential small molecule-binding sites in proteins”, Journal of Molecular Graphics and Modelling, Vol.15, hal. 359 –363, , (1997),
Nugroho, SA, Witarto, AB, Handoko, D, “Application of Support Vector Machine in Bioinformatics”, Proceeding of Indonesian Scientific Meeting in Central Japan, December 20, 2003.
Mahdiyah, Umi, Irawan, Isa, dan Imah,EM, “Study Comparison Backpropogation, Support Vector Machine, and Extreme Learning Machine for Bioinformatics Data”, Journal of Computer Science and Information, Vol 8: No 1, 53-59, 2015a.
Mahdiyah, Umi, Imah,EM, dan Irawan, “Integrating Data Selection and Extreme Learning Machine to Predict Protein-Ligand Binding Site”, Contemporary Engineering Sciences, Vol. 9, no. 16, 791 – 797,2016.
Shen, Shiyi dan Jack A. Tuszynski, Theory and Mathematical Methods for Bioformatics, Springer, Verlag Berlin Heidelberg, 2008.
Wang, Debby D., Wang, Ran dan Hong Yan, “Fast prediction of protein–protein interaction sites based on Extreme Learning Machines”, Neurocomputing,Vol. 128, hal. 258–266, 2014.
Wang, Dianhui dan Guang-Bin Huang, “Protein Sequence Classification Using Extreme Learning Machine”, Proceedings of International Joint Conference on Neural Networks, Montreal, Canada,hal. 1406-1411, 2005.
Xie, Z.R. and Hwang, M.J. (2012) Ligand-binding site prediction using ligand-interacting and binding site-enriched protein triangles. Bioinformatics, 28, 1579-1585.
Zhu, Chengzhang, Yin, Jianping dan Qian Li, “A Stock Decision Support System Based on ELM”, Proceedings of the International Conference on Extreme Learning Machines (ELM2013), (eds) Sun, F., Toh, K.-A., Romay, M.G., Mao, K., Beijing, hal.67-79, 2013.
Zvelebil, Marketa dan Jeremy O. Baum, “Understanding Bioinformatics”, Garland Science, New York, 2008.
DOI: http://dx.doi.org/10.12962/limits.v14i2.3079
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Limits: Journal Mathematics and its Aplications by Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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