Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression

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@Article{Chen:2011:TCBB,
  author =       "Carla Chia-Ming Chen and Holger Schwender and 
                 Jonathan Keith and Robin Nunkesser and Kerrie Mengersen and 
                 Paula Macrossan",
  title =        "Methods for Identifying SNP Interactions: A Review on
                 Variations of Logic Regression, Random Forest and
                 {Bayesian} Logistic Regression",
  journal =      "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  year =         "2011",
  volume =       "8",
  number =       "6",
  pages =        "1580--1591",
  month =        nov # "-" # dec,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Logic regressions, Genetic
                 Programming for Association Studies, Modified Logic
                 Regression-Gene Expression Programming, Random Forest,
                 Bayesian logistic regression with stochastic search
                 algorithm, candidate gene search",
  ISSN =         "1545-5963",
  DOI =          "doi:10.1109/TCBB.2011.46",
  size =         "12 pages",
  abstract =     "Due to advancements in computational ability, enhanced
                 technology and a reduction i the price of genotyping,
                 more data are being generated for understanding genetic
                 associations with diseases and disorders. However, with
                 the availability of large data sets comes the inherent
                 challenges of new methods of statistical analysis and
                 modelling. Considering a complex phenotype may be the
                 effect of a combination of multiple loci, various
                 statistical methods have been developed for identifying
                 genetic epistasis effects. Among these methods, logic
                 regression (LR) is an intriguing approach incorporating
                 tree-like structures. Various methods have built on the
                 original LR to improve different aspects of the model.
                 In this study, we review four variations of LR, namely
                 Logic Feature Selection, Monte Carlo Logic Regression,
                 Genetic Programming for Association Studies and
                 Modified Logic Regression-Gene Expression Programming,
                 and investigate the performance of each method using
                 simulated and real genotype data. We contrast these
                 with another tree-like approach, namely Random Forests,
                 and a Bayesian logistic regression with stochastic
                 search variable selection.",
  notes =        "Also known as \cite{5728791}",
}

Genetic Programming entries for Carla Chia-Ming Chen Holger Schwender Jonathan Keith Robin Nunkesser Kerrie Mengersen Paula Macrossan

Citations