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X Demographics
Mendeley readers
Attention Score in Context
Title |
Grammatical evolution decision trees for detecting gene-gene interactions
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Published in |
BioData Mining, November 2010
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DOI | 10.1186/1756-0381-3-8 |
Pubmed ID | |
Authors |
Alison A Motsinger-Reif, Sushamna Deodhar, Stacey J Winham, Nicholas E Hardison |
Abstract |
A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 67% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 3% |
Unknown | 37 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 29% |
Researcher | 9 | 24% |
Student > Master | 6 | 16% |
Student > Postgraduate | 3 | 8% |
Professor | 2 | 5% |
Other | 5 | 13% |
Unknown | 2 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 16 | 42% |
Engineering | 4 | 11% |
Agricultural and Biological Sciences | 4 | 11% |
Biochemistry, Genetics and Molecular Biology | 3 | 8% |
Medicine and Dentistry | 3 | 8% |
Other | 3 | 8% |
Unknown | 5 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 28 November 2012.
All research outputs
#13,675,566
of 22,687,320 outputs
Outputs from BioData Mining
#194
of 307 outputs
Outputs of similar age
#135,085
of 179,796 outputs
Outputs of similar age from BioData Mining
#2
of 2 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
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 is in the 36th percentile – i.e., 36% 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 179,796 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.