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
Link To Document :
بازگشت