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SNP interaction detection with Random Forests in high-dimensional genetic data

Overview of attention for article published in BMC Bioinformatics, July 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
75 Dimensions

Readers on

mendeley
130 Mendeley
citeulike
2 CiteULike
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Title
SNP interaction detection with Random Forests in high-dimensional genetic data
Published in
BMC Bioinformatics, July 2012
DOI 10.1186/1471-2105-13-164
Pubmed ID
Authors

Stacey J Winham, Colin L Colby, Robert R Freimuth, Xin Wang, Mariza de Andrade, Marianne Huebner, Joanna M Biernacka

Abstract

Identifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters 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 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Belgium 2 2%
Netherlands 1 <1%
Brazil 1 <1%
Malaysia 1 <1%
India 1 <1%
Turkey 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Other 0 0%
Unknown 118 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 29%
Researcher 28 22%
Student > Master 21 16%
Professor > Associate Professor 8 6%
Student > Bachelor 7 5%
Other 20 15%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 22%
Computer Science 21 16%
Biochemistry, Genetics and Molecular Biology 18 14%
Mathematics 10 8%
Engineering 9 7%
Other 29 22%
Unknown 15 12%

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 03 October 2012.
All research outputs
#6,298,375
of 21,334,388 outputs
Outputs from BMC Bioinformatics
#2,459
of 6,922 outputs
Outputs of similar age
#41,849
of 142,640 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 16 outputs
Altmetric has tracked 21,334,388 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 6,922 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 63% 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 142,640 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 70% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.