A human papillomavirus type plays an important role in the early diagnosis of cervical cancer. Most of the prediction methods use protein sequence and structure information, but the reduced amino acid modes have not been used until now. In this paper, we introduced the modes of reduced amino acids to predict high-risk HPV. We first reduced 20 amino acids into several nonoverlapping groups and calculated their structure and physicochemical modes for high-risk HPV prediction, which was tested and compared with the existing methods on 68 samples of known HPV types. The experiment result indicates that the proposed method achieved better performance with an accuracy of 96.49%, indicating that the reduced amino acid modes might be used to improve the prediction of high-risk HPV types.

中文翻译：

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使用减少的氨基酸模式预测人类乳头瘤病毒的高风险类型。

人乳头瘤病毒类型在子宫颈癌的早期诊断中起重要作用。大多数预测方法都使用蛋白质序列和结构信息，但是直到现在还没有使用还原的氨基酸模式。在本文中，我们介绍了还原氨基酸的模式来预测高危HPV。我们首先将20个氨基酸还原为几个不重叠的组，并计算了它们的结构和理化模式以预测高危HPV，然后对68种已知HPV类型的样品进行了测试并与现有方法进行了比较。实验结果表明，该方法取得了较好的性能，准确率达96.49％，表明氨基酸还原模式可以提高对高危HPV类型的预测。