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SNPInterForest: A new method for detecting epistatic interactions

Overview of attention for article published in BMC Bioinformatics, December 2011
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
100 Mendeley
citeulike
2 CiteULike
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Title
SNPInterForest: A new method for detecting epistatic interactions
Published in
BMC Bioinformatics, December 2011
DOI 10.1186/1471-2105-12-469
Pubmed ID
Authors

Makiko Yoshida, Asako Koike

Abstract

Multiple genetic factors and their interactive effects are speculated to contribute to complex diseases. Detecting such genetic interactive effects, i.e., epistatic interactions, however, remains a significant challenge in large-scale association studies.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Germany 2 2%
Brazil 2 2%
Italy 1 1%
Japan 1 1%
India 1 1%
Unknown 90 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 33%
Researcher 26 26%
Student > Master 9 9%
Professor > Associate Professor 8 8%
Professor 4 4%
Other 8 8%
Unknown 12 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 23%
Computer Science 15 15%
Biochemistry, Genetics and Molecular Biology 11 11%
Mathematics 10 10%
Medicine and Dentistry 9 9%
Other 14 14%
Unknown 18 18%

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 13 December 2011.
All research outputs
#2,327,738
of 4,505,992 outputs
Outputs from BMC Bioinformatics
#1,787
of 2,646 outputs
Outputs of similar age
#100,179
of 232,609 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 98 outputs
Altmetric has tracked 4,505,992 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% 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 4.3. This one is in the 22nd percentile – i.e., 22% 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 232,609 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 98 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.