A support vector machine approach for detecting gene-gene interaction

Shyh Huei Chen, Jielin Sun, Latchezar Dimitrov, Aubrey R. Turner, Tamara S. Adams, Deborah A. Meyers, Bao Li Chang, S. Lilly Zheng, Henrik Grönberg, Jianfeng Xu, Fang Chi Hsu

Research output: Contribution to journalArticlepeer-review

93 Scopus citations

Abstract

Although genetic factors play an important role in most human diseases, multiple genes or genes and environmental factors may influence individual risk. In order to understand the underlying biological mechanisms of complex diseases, it is important to understand the complex relationships that control the process. In this paper, we consider different perspectives, from each optimization, complexity analysis, and algorithmic design, which allows us to describe a reasonable and applicable computational framework for detecting gene-gene interactions. Accordingly, support vector machine and combinatorial optimization techniques (local search and genetic algorithm) were tailored to fit within this framework. Although the proposed approach is computationally expensive, our results indicate this is a promising tool for the identification and characterization of high order gene-gene and gene-environment interactions. We have demonstrated several advantages of this method, including the strong power for classification, less concern for overfitting, and the ability to handle unbalanced data and achieve more stable models. We would like to make the support vector machine and combinatorial optimization techniques more accessible to genetic epidemiologists, and to promote the use and extension of these powerful approaches.

Original languageEnglish (US)
Pages (from-to)152-167
Number of pages16
JournalGenetic Epidemiology
Volume32
Issue number2
DOIs
StatePublished - Feb 2008
Externally publishedYes

Keywords

  • Association analysis
  • Data mining
  • Gene-gene interaction
  • SNPs
  • Support vector machine

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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