ArticleComputational and mathematical methods in medicine2015
Prediction of high-risk types of human papillomaviruses using statistical model of protein "sequence space".
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Novel Method for Predicting Oncogenic Types of Human Papillomavirus.Diagnostics (Basel, Switzerland) · 2025Article
- MicroRNA let-7 and viral infections: focus on mechanisms of action.Cellular & molecular biology letters · 2022Review
- Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses.Computational and mathematical methods in medicine · 2022Article
- Polyploid Giant Cancer Cells, a Hallmark of Oncoviruses and a New Therapeutic Challenge.Frontiers in oncology · 2020Review
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7 authors.
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Abstract
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.
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