Identifying the Combination of Genetic Factors That Determine Susceptibility to Cervical Cancer

Jorng Tzong Horng, K. C. Hu, Li Cheng Wu, Hsien Da Huang, Feng Mao Lin, S. L. Huang, H. C. Lai, T. Y. Chu

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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/.

Original languageEnglish
Pages (from-to)59-66
Number of pages8
JournalIEEE Transactions on Information Technology in Biomedicine
Volume8
Issue number1
DOIs
Publication statusPublished - Mar 2004
Externally publishedYes

Keywords

  • Bayesian network
  • Cervical cancer
  • Decision tree
  • Genetic factors

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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