• DocumentCode
    918977
  • Title

    Identifying the combination of genetic factors that determine susceptibility to cervical cancer

  • Author

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

  • Author_Institution
    Dept. of Life Sci., Nat. Central Univ., Jhongli City, Taiwan
  • Volume
    8
  • Issue
    1
  • fYear
    2004
  • fDate
    3/1/2004 12:00:00 AM
  • Firstpage
    59
  • Lastpage
    66
  • 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/.
  • Keywords
    belief networks; cancer; cellular biophysics; decision trees; genetics; gynaecology; medical computing; microorganisms; polymorphism; Bayesian network; SNP; cervical cancer hereditary component; complex multifactorial disease; decision tree algorithms; genetic factors; genetic susceptibility; human papillo-mavirus infection; learning algorithms; microsatellites; precancerous change; single-nucleotide polymorphism markers; Amino acids; Bioinformatics; Cervical cancer; Cities and towns; Computer science; Decision trees; Genetics; Humans; Lesions; Testing; Algorithms; Bayes Theorem; Case-Control Studies; Diagnosis, Computer-Assisted; Female; Gene Expression Profiling; Genetic Predisposition to Disease; Genetic Screening; Humans; Internet; Microsatellite Repeats; Phylogeny; Polymorphism, Single Nucleotide; Reproducibility of Results; Retrospective Studies; Risk Assessment; Sensitivity and Specificity; Uterine Cervical Neoplasms;
  • fLanguage
    English
  • Journal_Title
    Information Technology in Biomedicine, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1089-7771
  • Type

    jour

  • DOI
    10.1109/TITB.2004.824738
  • Filename
    1271301