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MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study

Overview of attention for article published in BMC Bioinformatics, January 2009
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Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
57 Dimensions

Readers on

mendeley
76 Mendeley
citeulike
8 CiteULike
connotea
1 Connotea
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Title
MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study
Published in
BMC Bioinformatics, January 2009
DOI 10.1186/1471-2105-10-13
Pubmed ID
Authors

Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson LS Tang, Weichuan Yu

Abstract

The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.

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

Geographical breakdown

Country Count As %
Brazil 3 4%
United States 2 3%
United Kingdom 1 1%
New Zealand 1 1%
Portugal 1 1%
Unknown 68 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 32%
Student > Ph. D. Student 19 25%
Student > Master 7 9%
Professor > Associate Professor 6 8%
Professor 5 7%
Other 10 13%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 39%
Computer Science 14 18%
Medicine and Dentistry 7 9%
Biochemistry, Genetics and Molecular Biology 7 9%
Engineering 3 4%
Other 4 5%
Unknown 11 14%

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 23 June 2013.
All research outputs
#2,278,906
of 4,508,238 outputs
Outputs from BMC Bioinformatics
#1,785
of 2,646 outputs
Outputs of similar age
#44,437
of 89,496 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 86 outputs
Altmetric has tracked 4,508,238 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,646 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 89,496 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 86 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.