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A comparison of internal model validation methods for multifactor dimensionality reduction in the case of genetic heterogeneity

Overview of attention for article published in BMC Research Notes, November 2012
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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1 X user
wikipedia
1 Wikipedia page

Citations

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5 Dimensions

Readers on

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14 Mendeley
Title
A comparison of internal model validation methods for multifactor dimensionality reduction in the case of genetic heterogeneity
Published in
BMC Research Notes, November 2012
DOI 10.1186/1756-0500-5-623
Pubmed ID
Authors

Jeffrey J Gory, Holly C Sweeney, David M Reif, Alison A Motsinger-Reif

Abstract

Determining the genes responsible for certain human traits can be challenging when the underlying genetic model takes a complicated form such as heterogeneity (in which different genetic models can result in the same trait) or epistasis (in which genes interact with other genes and the environment). Multifactor Dimensionality Reduction (MDR) is a widely used method that effectively detects epistasis; however, it does not perform well in the presence of heterogeneity partly due to its reliance on cross-validation for internal model validation. Cross-validation allows for only one "best" model and is therefore inadequate when more than one model could cause the same trait. We hypothesize that another internal model validation method known as a three-way split will be better at detecting heterogeneity models.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 7%
Germany 1 7%
Unknown 12 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 21%
Professor > Associate Professor 3 21%
Student > Bachelor 2 14%
Professor 2 14%
Researcher 2 14%
Other 2 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 21%
Engineering 3 21%
Computer Science 2 14%
Environmental Science 1 7%
Mathematics 1 7%
Other 3 21%
Unknown 1 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 14 May 2017.
All research outputs
#6,917,125
of 22,685,926 outputs
Outputs from BMC Research Notes
#1,096
of 4,253 outputs
Outputs of similar age
#53,103
of 183,395 outputs
Outputs of similar age from BMC Research Notes
#20
of 74 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 4,253 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 183,395 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.