TAG SNP selection using GA-SVM approach
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Investigations 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. In this study, a new method called GA-SVM was introduced, where Support Vector Machine (SVM) and Genetic Algorithm (GA) are used for SNP prediction and for tag SNP selection, respectively. The suggested method was compared with existing tag SNP selection algorithms over different datasets. Experimental results demonstrated that the suggested method could identify tag SNPs with better prediction accuracy than other methods. © 2011 IADIS.
Açıklama
IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011 -- 24 July 2011 through 26 July 2011 -- Rome -- 91770
Anahtar Kelimeler
Genetic algorithm (GA), Single nucleotide polymorphisms (SNPs), SVM, Tag SNPs
Kaynak
Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
WoS Q Değeri
Scopus Q Değeri
N/A
