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Attention Score in Context
| Title |
A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection
|
|---|---|
| Published in |
BioData Mining, June 2014
|
| DOI | 10.1186/1756-0381-7-8 |
| Pubmed ID | |
| Authors |
Ryan J Urbanowicz, Ambrose LS Granizo-Mackenzie, Jeff Kiralis, Jason H Moore |
| Abstract |
The statistical genetics phenomenon of epistasis is widely acknowledged to confound disease etiology. In order to evaluate strategies for detecting these complex multi-locus disease associations, simulation studies are required. The development of the GAMETES software for the generation of complex genetic models, has provided the means to randomly generate an architecturally diverse population of epistatic models that are both pure and strict, i.e. all n loci, but no fewer, are predictive of phenotype. Previous theoretical work characterizing complex genetic models has yet to examine pure, strict, epistasis which should be the most challenging to detect. This study addresses three goals: (1) Classify and characterize pure, strict, two-locus epistatic models, (2) Investigate the effect of model 'architecture' on detection difficulty, and (3) Explore how adjusting GAMETES constraints influences diversity in the generated models. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 7% |
| Unknown | 14 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 27% |
| Researcher | 4 | 27% |
| Student > Postgraduate | 3 | 20% |
| Professor > Associate Professor | 2 | 13% |
| Student > Master | 2 | 13% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 27% |
| Medicine and Dentistry | 4 | 27% |
| Agricultural and Biological Sciences | 3 | 20% |
| Biochemistry, Genetics and Molecular Biology | 1 | 7% |
| Neuroscience | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 2 | 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 31 January 2015.
All research outputs
#13,488,874
of 22,903,988 outputs
Outputs from BioData Mining
#187
of 308 outputs
Outputs of similar age
#111,489
of 229,145 outputs
Outputs of similar age from BioData Mining
#7
of 8 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 308 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 37th percentile – i.e., 37% 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 229,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.