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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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Citations

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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.

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 83 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 2%
Portugal 1 1%
New Zealand 1 1%
United Kingdom 1 1%
Unknown 75 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 29%
Student > Ph. D. Student 20 24%
Student > Master 8 10%
Professor 6 7%
Professor > Associate Professor 6 7%
Other 12 14%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 36%
Computer Science 14 17%
Medicine and Dentistry 8 10%
Biochemistry, Genetics and Molecular Biology 7 8%
Engineering 3 4%
Other 7 8%
Unknown 14 17%
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 23 June 2013.
All research outputs
#15,273,442
of 22,712,476 outputs
Outputs from BMC Bioinformatics
#5,364
of 7,259 outputs
Outputs of similar age
#142,032
of 169,311 outputs
Outputs of similar age from BMC Bioinformatics
#52
of 65 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 169,311 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.