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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.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Ilhan I,Tezel G

doi

10.1016/j.jbi.2012.12.002

subject

Has Abstract

pub_date

2013-04-01 00:00:00

pages

328-40

issue

2

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(12)00185-2

journal_volume

46

pub_type

杂志文章

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