College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.
College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China.
Comput Math Methods Med. 2020 Jun 18;2020:5325304. doi: 10.1155/2020/5325304. eCollection 2020.
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.
人乳头瘤病毒类型在宫颈癌的早期诊断中起着重要作用。大多数预测方法都使用蛋白质序列和结构信息，但到目前为止还没有使用简化的氨基酸模式。在本文中，我们引入了简化氨基酸模式来预测高危 HPV。我们首先将 20 种氨基酸简化为几个不重叠的组，并计算了它们的结构和物理化学模式，以用于高危 HPV 的预测，并在 68 个已知 HPV 类型的样本上进行了测试和与现有方法进行了比较。实验结果表明，该方法的准确率达到了 96.49%，性能更好，表明简化的氨基酸模式可能用于改善高危 HPV 类型的预测。
