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Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2013
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Title
Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data
Published in
BMC Medical Informatics and Decision Making, April 2013
DOI 10.1186/1472-6947-13-s1-s3
Pubmed ID
Authors

Chiyong Kang, Hyeji Yu, Gwan-Su Yi

Abstract

Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.

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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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
United States 1 2%
Ecuador 1 2%
Unknown 43 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 24%
Student > Master 9 20%
Researcher 9 20%
Student > Doctoral Student 3 7%
Professor > Associate Professor 3 7%
Other 5 11%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 35%
Biochemistry, Genetics and Molecular Biology 6 13%
Medicine and Dentistry 6 13%
Engineering 2 4%
Psychology 2 4%
Other 4 9%
Unknown 10 22%
Attention Score in Context

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 05 July 2013.
All research outputs
#18,341,369
of 22,713,403 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,565
of 1,982 outputs
Outputs of similar age
#151,585
of 199,934 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#34
of 37 outputs
Altmetric has tracked 22,713,403 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.