Ilhan, IlhanGoktepe, Yunus EmreOzcan, CengizKahramanli, Sirzat2020-03-262020-03-262011978-0-7918-5971-1https://hdl.handle.net/20.500.12395/268793rd International Conference on Future Computer and Communication (ICFCC 2011) -- JUN 03-05, 2011 -- G Enescu Univ Arts, Iasi, ROMANIAInvestigations on genetic variants associated with complex diseases are important for enhancements in diagnosis and treatments. SNPs (Single Nucleotide Polymorphisms), which comprise most of the millions of changes in human genome, are promising tools for disease-gene association studies. On the other hand, these studies are limited by cost of genotyping tremendous number of SNPs. Therefore, it is essential to identify a subset of SNPs that represents rest of the SNPs. As subset of SNPs is identified, data set should be searched as well as possible. In this study, a new method called CLONTagger was introduced, where Support Vector Machine (SVM) was used as SNP prediction method, whereas Clonal Selection Algorithm (CLONALG) was used as tag SNP selection method. The suggested method was compared with current tag SNP selection algorithms in literature using different datasets. Experimental results demonstrated that the suggested method could identify tag SNPs with better prediction accuracy than other methods from literature.eninfo:eu-repo/semantics/closedAccessSingle Nucleotide Polymorphisms (SNPs)Tag SNPsClonal Selection Algorithm (CLONALG)Support Vector Machine (SVM)Conference Object47+WOS:000320410200008N/A