Abstract—Hantaviruses are single-stranded, enveloped, negative sense RNA viruses of theBunyaviridae family. They induce deadly hemorrhagic fever with fatality up to 40%. Currently, there is no specific cure for Hantaviruses, so more research about this mortal virus should be done. In order to effectively analyze a variety of different Hantaviruses, we utilize a model called the support vector machine (also known as SVM) which is generally used for analyzing and classifying binary data.The basic mechanism of the SVM is to find the most optimal hyperplane, or the maximum-margin hyperplane, which can separate different types of data with the least error bound. Out of all of the hyperplanes that may be used to classify the data points, the most optimal hyperplane is the one that has the largest margin, or separation between different types of data. In other words, the optimal hyperplane is chosen in the case where the distance between the nearest points of each group of data is maximized. Ultimately, using the selected hyperplane, SVM classifies the data points and computes values such as accuracy and sensitivity. At the end of its operation, the SVM algorithm prints out the computed values. SuchSVM algorithms can be used to learn the characteristics of each Hantavirus such as sequence patterns and abundance of amino acids. Since we are the first ever to scientifically investigate the Hantavirus with SVM, it is expected that the results of this research will be greatly helpful for further in-depth researching and development of the cure for the virus.

Index Terms—Accuracy, glycoprotein sequence, Hantavirus, SVM.

Yusin Kim is with the Hankuk Academy of Foreign Studies majoring natural science, Korea (e-mail: ushin612@naver.com).

Cite: Yusin Kim, Youngmin Ko, Daniel P. Jeong, and Taeseon Yoon, "Analysis of Hantaviruses Glycoprotein Sequence Using SVM Algorithm," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 4, no. 4, pp. 261-264, 2014.