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
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