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BioData Mining 2011
An R package implementation of multifactor dimensionality reductionAbstract: We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/ webcite. We also provide data examples to illustrate the use and functionality of the package.MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users.With advances in genotyping technologies, a breadth of high-dimensional data is now available with unprecedented numbers of genetic markers to perform association mapping in human genetics. Identifying variants associated with complex human traits is a common problem and data-mining approaches to variable selection are frequent methods of analysis. There is growing evidence that epistasis may play a role in disease risk, and many variable selection approaches have been developed to consider potential gene-gene and gene-environment interactions. One of the most commonly used techniques for case-control data is Multifactor Dimensionality Reduction (MDR), a nonparametric exhaustive search method that considers all combinations of potentially interacting loci and classifies individuals to disease status based on their genetic information [1]. MDR has been highly successful in human genetics, with a large number of associations identified in real data applications; additionally, the performance of the method has been extensively studied in a range of simulation experiments and has undergone numerous developments and extensions to improve performance [2,3].Currently software is available which implements the MDR method, including a GUI implementation available at http://www.epistasi
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