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Attention Score in Context
| Title |
Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases
|
|---|---|
| Published in |
BMC Bioinformatics, December 2008
|
| DOI | 10.1186/1471-2105-9-528 |
| Pubmed ID | |
| Authors |
Wei Yu, Anja Wulf, Tiebin Liu, Muin J Khoury, Marta Gwinn |
| Abstract |
Millions of single nucleotide polymorphisms have been identified as a result of the human genome project and the rapid advance of high throughput genotyping technology. Genetic association studies, such as recent genome-wide association studies (GWAS), have provided a springboard for exploring the contribution of inherited genetic variation and gene/environment interactions in relation to disease. Given the capacity of such studies to produce a plethora of information that may then be described in a number of publications, selecting possible disease susceptibility genes and identifying related modifiable risk factors is a major challenge. A Web-based application for finding evidence of such relationships is key to the development of follow-up studies and evidence for translational research. We developed a Web-based application that selects and prioritizes potential disease-related genes by using a highly curated and updated literature database of genetic association studies. The application, called Gene Prospector, also provides a comprehensive set of links to additional data sources. |
Mendeley readers
The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 4% |
| Switzerland | 1 | 1% |
| Italy | 1 | 1% |
| Germany | 1 | 1% |
| Belgium | 1 | 1% |
| Spain | 1 | 1% |
| Greece | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 85 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 33 | 34% |
| Student > Ph. D. Student | 23 | 24% |
| Student > Master | 8 | 8% |
| Professor | 6 | 6% |
| Other | 4 | 4% |
| Other | 14 | 15% |
| Unknown | 8 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 40 | 42% |
| Computer Science | 12 | 13% |
| Medicine and Dentistry | 12 | 13% |
| Biochemistry, Genetics and Molecular Biology | 10 | 10% |
| Engineering | 4 | 4% |
| Other | 4 | 4% |
| Unknown | 14 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 03 April 2009.
All research outputs
#2,860,890
of 22,707,247 outputs
Outputs from BMC Bioinformatics
#1,000
of 7,255 outputs
Outputs of similar age
#13,950
of 165,418 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 49 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,255 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 86% of its peers.
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 165,418 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.