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SNP selection in genome-wide association studies via penalized support vector machine with MAX test.

Publication ,  Journal Article
Kim, J; Sohn, I; Kim, DDH; Jung, S-H
Published in: Comput Math Methods Med
2013

One of main objectives of a genome-wide association study (GWAS) is to develop a prediction model for a binary clinical outcome using single-nucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods have been widely used toward this end. However, since investigators often ignore the genetic models of SNPs, a final model results in a loss of efficiency in prediction of the clinical outcome. In order to overcome this problem, we propose a two-stage method such that the the genetic models of each SNP are identified using the MAX test and then a prediction model is fitted using a penalized SVM method. We apply the proposed method to various penalized SVMs and compare the performance of SVMs using various penalty functions. The results from simulations and real GWAS data analysis show that the proposed method performs better than the prediction methods ignoring the genetic models in terms of prediction power and selectivity.

Duke Scholars

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Published In

Comput Math Methods Med

DOI

EISSN

1748-6718

Publication Date

2013

Volume

2013

Start / End Page

340678

Location

United States

Related Subject Headings

  • Support Vector Machine
  • Prognosis
  • Polymorphism, Single Nucleotide
  • Models, Genetic
  • Logistic Models
  • Humans
  • Genome-Wide Association Study
  • Biostatistics
  • Bioinformatics
  • 4901 Applied mathematics
 

Citation

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Kim, J., Sohn, I., Kim, D. D. H., & Jung, S.-H. (2013). SNP selection in genome-wide association studies via penalized support vector machine with MAX test. Comput Math Methods Med, 2013, 340678. https://doi.org/10.1155/2013/340678
Kim, Jinseog, Insuk Sohn, Dennis Dong Hwan Kim, and Sin-Ho Jung. “SNP selection in genome-wide association studies via penalized support vector machine with MAX test.Comput Math Methods Med 2013 (2013): 340678. https://doi.org/10.1155/2013/340678.
Kim J, Sohn I, Kim DDH, Jung S-H. SNP selection in genome-wide association studies via penalized support vector machine with MAX test. Comput Math Methods Med. 2013;2013:340678.
Kim, Jinseog, et al. “SNP selection in genome-wide association studies via penalized support vector machine with MAX test.Comput Math Methods Med, vol. 2013, 2013, p. 340678. Pubmed, doi:10.1155/2013/340678.
Kim J, Sohn I, Kim DDH, Jung S-H. SNP selection in genome-wide association studies via penalized support vector machine with MAX test. Comput Math Methods Med. 2013;2013:340678.

Published In

Comput Math Methods Med

DOI

EISSN

1748-6718

Publication Date

2013

Volume

2013

Start / End Page

340678

Location

United States

Related Subject Headings

  • Support Vector Machine
  • Prognosis
  • Polymorphism, Single Nucleotide
  • Models, Genetic
  • Logistic Models
  • Humans
  • Genome-Wide Association Study
  • Biostatistics
  • Bioinformatics
  • 4901 Applied mathematics