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Genomics Inform > Volume 16(4); 2018 > Article
DOI: https://doi.org/10.5808/GI.2018.16.4.e37    Published online December 28, 2018.
EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution
Sangseob Leem, Taesung Park
Department of Statistics, Seoul National University, Seoul 08826, Korea
Corresponding author:  Taesung Park
Tel: +82-2-880-8924   Fax: +82-2-883-6144   Email: tspark@stats.snu.ac.kr
Received: December 6, 2018   Accepted: December 16, 2018
Abstract
Gene-gene interaction is a key factor to explain the missing heritability. Many methods have been proposed to identify gene-gene interactions. Multi-factor dimensionality reduction (MDR) is a well-known method for gene-gene interaction detection by reduction from genotypes of SNP combination to a binary variable with a value of high risk or low risk. This method is widely expanded to own a specific objective. Among those expansions, fuzzy-MDR used the fuzzy-set theory for the membership of high risk or low risk and increase the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow because it is implemented by R script language. Therefore, in this study, we implement EFMDR using RCPP (c++ package) for faster executions. Our implementation for faster EFMDR called EMMDR-Fast is about 800 times faster than EFMDR written by R script only.
Keywords: gene-gene interaction, MDR, Fuzzy-MDR, EFMDR, RCPP
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