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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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