摘要
Cervical cancer is common among women all over the world. Although infection with high-risk types of human papillomavirus (HPV) has been identified as the primary cause of cervical cancer, only some of those infected go on to develop cervical cancer. Obviously, the progression from HPV infection to cancer involves other environmental and host factors. Recent population-based twin and family studies have demonstrated the importance of the hereditary component of cervical cancer, associated with genetic susceptibility. Consequently, single-nucleotide polymorphism (SNP) markers and microsatellites should be considered genetic factors for determining what combinations of genetic factors are involved in precancerous changes to cervical cancer. This study employs a Bayesian network and four different decision tree algorithms, and compares the performance of these learning algorithms. The results of this study raise the possibility of investigations that could identify combinations of genetic factors, such as SNPs and microsatellites, that influence the risk associated with common complex multifactorial diseases, such as cervical cancer. The web site associated with this study is http://140.115.155.8/FactorAnalysis/.
| 原文 | 英語 |
|---|---|
| 頁(從 - 到) | 59-66 |
| 頁數 | 8 |
| 期刊 | IEEE Transactions on Information Technology in Biomedicine |
| 卷 | 8 |
| 發行號 | 1 |
| DOIs | |
| 出版狀態 | 已發佈 - 3月 2004 |
| 對外發佈 | 是 |
UN SDG
此研究成果有助於以下永續發展目標
-
SDG 3 良好的健康和福祉
ASJC Scopus subject areas
- 電氣與電子工程
- 生物技術
- 電腦科學應用
指紋
深入研究「Identifying the Combination of Genetic Factors That Determine Susceptibility to Cervical Cancer」主題。共同形成了獨特的指紋。引用此
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