Analysis of High-Risk Human Papillomavirus Using Decision Tree and Apriori Algorithm

被引：2

作者：

Lee, Da Eun ^{[1
]}

Yoon, Taeseon ^{[2
]}

机构：

[1] Hankuk Acad Foreign Studies, Dept Int Course, 50 54 Beon Gil, Yongin, Gyeonggido, South Korea

[2] Korea Univ, Dept Comp Sci & Engn, 50 54 Beon Gil, Yongin, Gyeonggido, South Korea

来源：

ICCBB 2018: PROCEEDINGS OF THE 2018 2ND INTERNATIONAL CONFERENCE ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | 2018年

关键词：

Human papillomavirus; HPV; Cervical cancer; Decision tree; Apriori; Datamining;

D O I：

10.1145/3290818.3290830

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Human Papillomaviruses (HPV) are small, nonenveloped, double-stranded DNA and function as pathogens that infect epithelial surfaces of humans and animals. Undetected and left not treated, HPV can develop into cervical cancer. This study aims to distinguish different types of high-risk HPV by analyzing the DNA sequences. With Decision Tree and Apriori Algorithm, the study analyzes both common and distinctive features of HPV Type 16, 18, and 58. We concluded that with the method to distinguish different HPV types, more type-specific vaccines and treatments can be developed to treat cancers that are caused by HPV, including, but not limited to, cervical cancer and oral cancer.

引用

页码：22 / 26

页数：5

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