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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility
|
|---|---|
| Published in |
BioData Mining, April 2014
|
| DOI | 10.1186/1756-0381-7-5 |
| Pubmed ID | |
| Authors |
Ting Hu, Qinxin Pan, Angeline S Andrew, Jillian M Langer, Michael D Cole, Craig R Tomlinson, Margaret R Karagas, Jason H Moore |
| Abstract |
Several different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility. |
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 % |
|---|---|---|
| Finland | 1 | 25% |
| Spain | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 32 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 25% |
| Researcher | 8 | 25% |
| Student > Master | 5 | 16% |
| Student > Postgraduate | 3 | 9% |
| Professor | 2 | 6% |
| Other | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 34% |
| Biochemistry, Genetics and Molecular Biology | 7 | 22% |
| Computer Science | 5 | 16% |
| Medicine and Dentistry | 2 | 6% |
| Mathematics | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 2 | 6% |
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 13 September 2014.
All research outputs
#13,914,121
of 22,753,345 outputs
Outputs from BioData Mining
#197
of 307 outputs
Outputs of similar age
#117,409
of 226,967 outputs
Outputs of similar age from BioData Mining
#3
of 6 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% 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 33rd percentile – i.e., 33% 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 226,967 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.