Abstract
Human Papillomavirus (HPV) is known as the main cause of cervical cancer and classified to low- or high-risk type by its malignant potential. Detection of high-risk HPVs is critical to understand the mechanisms and recognize potential patients in medical judgments. In this paper, we present a simple kernel approach to classify HPV risk types from E6 protein sequences. Our method uses support vector machines combined with gap-spectrum kernels. The gap-spectrum kernel is introduced to compute the similarity between amino acids pairs with a fixed distance, which can be useful for the helical structure of proteins. In the experiments, the proposed method is compared with a mismatch kernel approach in accuracy and F1-score, and the predictions for unknown types are presented.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, S., Eom, JH. (2006). Prediction of the Human Papillomavirus Risk Types Using Gap-Spectrum Kernels. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_104
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DOI: https://doi.org/10.1007/11760191_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
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