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A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection

Overview of attention for article published in BioData Mining, April 2013
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Title
A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection
Published in
BioData Mining, April 2013
DOI 10.1186/1756-0381-6-9
Pubmed ID
Authors

Jestinah M Mahachie John, François Van Lishout, Elena S Gusareva, Kristel Van Steen

Abstract

Applying a statistical method implies identifying underlying (model) assumptions and checking their validity in the particular context. One of these contexts is association modeling for epistasis detection. Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates. Remedial measures for violated underlying conditions or assumptions include data transformation or selecting a more relaxed modeling or testing strategy. Model-Based Multifactor Dimensionality Reduction (MB-MDR) for epistasis detection relies on association testing between a trait and a factor consisting of multilocus genotype information. For quantitative traits, the framework is essentially Analysis of Variance (ANOVA) that decomposes the variability in the trait amongst the different factors. In this study, we assess through simulations, the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect 2-locus epistasis signals in the absence of main effects.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
India 1 3%
Romania 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 22%
Researcher 5 14%
Professor > Associate Professor 3 8%
Student > Master 3 8%
Student > Doctoral Student 2 6%
Other 8 22%
Unknown 7 19%
Readers by discipline Count As %
Engineering 5 14%
Agricultural and Biological Sciences 5 14%
Biochemistry, Genetics and Molecular Biology 3 8%
Mathematics 2 6%
Social Sciences 2 6%
Other 11 31%
Unknown 8 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2013.
All research outputs
#9,339,892
of 10,616,067 outputs
Outputs from BioData Mining
#195
of 208 outputs
Outputs of similar age
#104,194
of 127,016 outputs
Outputs of similar age from BioData Mining
#6
of 6 outputs
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