Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes

Joint Authors

Liu, Xiaoqing
Xu, Xinnan
Kong, Rui
He, Ping-an
Dai, Qi

Source

Computational and Mathematical Methods in Medicine

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-06-18

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Medicine

Abstract EN

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.

American Psychological Association (APA)

Xu, Xinnan& Kong, Rui& Liu, Xiaoqing& He, Ping-an& Dai, Qi. 2020. Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139469

Modern Language Association (MLA)

Xu, Xinnan…[et al.]. Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1139469

American Medical Association (AMA)

Xu, Xinnan& Kong, Rui& Liu, Xiaoqing& He, Ping-an& Dai, Qi. Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139469

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1139469