Back to Search
Start Over
Prediction of the Human Papillomavirus Risk Types Using Gap-Spectrum Kernels.
- Source :
- Advances in Neural Networks - ISNN 2006 (9783540344827); 2006, p710-715, 6p
- Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540344827
- Database :
- Supplemental Index
- Journal :
- Advances in Neural Networks - ISNN 2006 (9783540344827)
- Publication Type :
- Book
- Accession number :
- 32862475
- Full Text :
- https://doi.org/10.1007/11760191_104