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,
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author = "Carla Chia-Ming Chen and Holger Schwender and
Jonathan Keith and Robin Nunkesser and Kerrie Mengersen and
Paula Macrossan",
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title = "Methods for Identifying SNP Interactions: A Review on
Variations of Logic Regression, Random Forest and
{Bayesian} Logistic Regression",
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journal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
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year = "2011",
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volume = "8",
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number = "6",
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pages = "1580--1591",
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month = nov # "-" # dec,
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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",
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ISSN = "1545-5963",
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DOI = "doi:10.1109/TCBB.2011.46",
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size = "12 pages",
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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.",
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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