Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11760191_104 | ISNN (2) |
Keywords | Field | DocType |
mismatch kernel approach,malignant potential,gap-spectrum kernel,human papillomavirus risk type,high-risk type,high-risk hpvs,simple kernel approach,hpv risk type,e6 protein sequence,potential patient,protein sequence,spectrum,amino acid,support vector machine | Kernel (linear algebra),Similitude,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Kernel method,Artificial neural network,Protein function prediction,Machine learning,Statistical analysis | Conference |
Volume | ISSN | ISBN |
3973 | 0302-9743 | 3-540-34482-9 |
Citations | PageRank | References |
2 | 0.38 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sun Kim | 1 | 683 | 70.28 |
| Jae-Hong Eom | 2 | 86 | 8.91 |