Paper Info
Title
A genetic algorithm-support vector machine method with parameter optimization for selecting the tag SNPs.
Abstract
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 γ 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.
Year
DOI
Venue
2013
10.1016/j.jbi.2012.12.002
Journal of Biomedical Informatics
Keywords
Field
DocType
brand new method,genetic algorithm-support vector machine,tag snps,support vector machine,genetic algorithm,convenient subset,parameter optimization,low prediction accuracy,method benefit,better prediction accuracy,particle swarm optimization
Particle swarm optimization,Data mining,Genotyping,Computer science,Support vector machine,Genetic association,Artificial intelligence,Single-nucleotide polymorphism,Human genome,Genetic algorithm,Machine learning
Journal
Volume
Issue
ISSN
46
2
1532-0480
Citations 
PageRank 
References 
11
0.86
23
Authors
2
Name
Order
Citations
PageRank
Ilhan Ilhan1121.90
Gülay Tezel21047.40