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Prediction of the Human Papillomavirus Risk Types Using Gap-Spectrum Kernels

  • Sun Kim
  • Jae-Hong Eom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

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.

Keywords

Support Vector Machine Cervical Cancer Risk Type Amino Acid Pair Protein Function Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sun Kim
    • 1
  • Jae-Hong Eom
    • 1
  1. 1.Biointelligence Laboratory, School of Computer Science and EngineeringSeoul National UniversitySeoulSouth Korea

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