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
Link To Document :
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