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Identifying genetic interactions associated with late-onset Alzheimer’s disease

Overview of attention for article published in BioData Mining, December 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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4 X users
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

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17 Dimensions

Readers on

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46 Mendeley
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Title
Identifying genetic interactions associated with late-onset Alzheimer’s disease
Published in
BioData Mining, December 2014
DOI 10.1186/s13040-014-0035-z
Pubmed ID
Authors

Charalampos S Floudas, Nara Um, M Ilyas Kamboh, Michael M Barmada, Shyam Visweswaran

Abstract

Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 2%
United States 1 2%
Sweden 1 2%
Brazil 1 2%
Unknown 42 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Researcher 7 15%
Student > Master 7 15%
Student > Bachelor 4 9%
Student > Postgraduate 3 7%
Other 9 20%
Unknown 7 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 20%
Computer Science 6 13%
Neuroscience 6 13%
Medicine and Dentistry 4 9%
Biochemistry, Genetics and Molecular Biology 3 7%
Other 9 20%
Unknown 9 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 29 September 2019.
All research outputs
#4,553,087
of 22,778,347 outputs
Outputs from BioData Mining
#105
of 307 outputs
Outputs of similar age
#65,453
of 353,136 outputs
Outputs of similar age from BioData Mining
#3
of 12 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 65% 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 353,136 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.