ArticleGenomics & informatics2018
EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution.
Article in Genomics & informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes.BioMed research international · 2019Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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