Prediction of the human papillomavirus risk types using gap-spectrum kernels

Authors:
Sun Kim;Jae-Hong Eom
Affiliations:
Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, South Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
Cited 0

Fast String Kernels using Inexact Matching for Protein Sequences

The Journal of Machine Learning Research
Mismatch string kernels for discriminative protein classification

Bioinformatics

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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.