Prediction of the Human Papillomavirus Risk Types Using Gap-Spectrum Kernels
Identifieur interne : 002E02 ( Main/Exploration ); précédent : 002E01; suivant : 002E03Prediction of the Human Papillomavirus Risk Types Using Gap-Spectrum Kernels
Auteurs : Sun Kim [Corée du Sud] ; Jae-Hong Eom [Corée du Sud]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
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.
Url:
DOI: 10.1007/11760191_104
Affiliations:
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