• DocumentCode
    2790245
  • Title

    Predicting Susceptibility to Chronic Hepatitis using Single Nucleotide Polymorphism Data and Support Vector Machine

  • Author

    Kim, Dong-Hoi ; Uhmn, Saangyong ; Kim, Jin ; Cho, Sung Won ; Hahm, Ki-Baik

  • Author_Institution
    Hallym University, Korea
  • Volume
    2
  • fYear
    2006
  • fDate
    9-11 Nov. 2006
  • Firstpage
    31
  • Lastpage
    35
  • Abstract
    SVM(Support VectorMachine) is used to predict the susceptibility to Chronic Hepatitis from SNP(single nucleotide polymorphism) data. SVM is trained to predict the susceptibility using SNPs. SVM is able to distinguish Hepatitis between normal and Chronic Hepatitis with an accuracy of 75.61% which are much better than random guessing. With more SNPs and other features, SVM prediction using SNP data can be a potential tool for predicting susceptibility to Chronic Hepatitis.
  • Keywords
    Accuracy; Bioinformatics; Biological cells; Cancer; DNA; Genomics; Liver diseases; Sequences; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Information Technology, 2006. ICHIT '06. International Conference on
  • Conference_Location
    Cheju Island
  • Print_ISBN
    0-7695-2674-8
  • Type

    conf

  • DOI
    10.1109/ICHIT.2006.253585
  • Filename
    4021190