Prediction of the Risk Types of Human Papillomaviruses by Support Vector Machines
Identifieur interne : 003126 ( Main/Exploration ); précédent : 003125; suivant : 003127Prediction of the Risk Types of Human Papillomaviruses by Support Vector Machines
Auteurs : Je-Gun Joung [Corée du Sud] ; Sok June O [Corée du Sud] ; Byoung-Tak Zhang [Corée du Sud]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Infection by high-risk human papillomaviruses (HPVs) is associated with the development of cervical cancers. Classification of risk types is important to understand the mechanisms in infection and to develop novel instruments for medical examination such as DNA microarrays. In this paper, we classify the risk type of HPVs by using the protein sequences. Our approach is based on the hidden Markov model and the Support Vector Machines. The former searches informative subsequence positions and the latter computes efficiently to classify protein sequences. In the experiments, the proposed classifier was compared with previous methods in accuracy and F-cost, also the prediction result of four unknown types is presented.
Url:
DOI: 10.1007/978-3-540-28633-2_76
Affiliations:
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Classification of risk types is important to understand the mechanisms in infection and to develop novel instruments for medical examination such as DNA microarrays. In this paper, we classify the risk type of HPVs by using the protein sequences. Our approach is based on the hidden Markov model and the Support Vector Machines. The former searches informative subsequence positions and the latter computes efficiently to classify protein sequences. 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