A genetic algorithm-support vector machine method with parameter optimization for selecting the tag SNPs
Küçük Resim Yok
Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ACADEMIC PRESS INC ELSEVIER SCIENCE
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
SNPs (Single Nucleotide Polymorphisms) include millions of changes in human genome, and therefore, are promising tools for disease-gene association studies. However, this kind of studies is constrained by the high expense of genotyping millions of SNPs. For this reason, it is required to obtain a suitable subset of SNPs to accurately represent the rest of SNPs. For this purpose, many methods have been developed to select a convenient subset of tag SNPs, but all of them only provide low prediction accuracy. In the present study, a brand new method is developed and introduced as GA-SVM with parameter optimization. This method benefits from support vector machine (SVM) and genetic algorithm (GA) to predict SNPs and to select tag SNPs, respectively. Furthermore, it also uses particle swarm optimization (PSO) algorithm to optimize C and gamma parameters of support vector machine. It is experimentally tested on a wide range of datasets, and the obtained results demonstrate that this method can provide better prediction accuracy in identifying tag SNPs compared to other methods at present. (c) 2012 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
Single Nucleotide Polymorphisms (SNPs), Tag SNPs, Genetic algorithm (GA), Support vector machine (SVM), Particle swarm optimization (PSO)
Kaynak
JOURNAL OF BIOMEDICAL INFORMATICS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
46
Sayı
2
