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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 756345, 9 pages
Research Article

Prediction of High-Risk Types of Human Papillomaviruses Using Statistical Model of Protein “Sequence Space”

1College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
2College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China
3Department of Gynecological Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China
4College of Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
5Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA

Received 19 September 2014; Accepted 31 March 2015

Academic Editor: Edward J. Perkins

Copyright © 2015 Cong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Discrimination of high-risk types of human papillomaviruses plays an important role in the diagnosis and remedy of cervical cancer. Recently, several computational methods have been proposed based on protein sequence-based and structure-based information, but the information of their related proteins has not been used until now. In this paper, we proposed using protein “sequence space” to explore this information and used it to predict high-risk types of HPVs. The proposed method was tested on 68 samples with known HPV types and 4 samples without HPV types and further compared with the available approaches. The results show that the proposed method achieved the best performance among all the evaluated methods with accuracy 95.59% and F1-score 90.91%, which indicates that protein “sequence space” could potentially be used to improve prediction of high-risk types of HPVs.