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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Learning virulent proteins from integrated query networks
|
|---|---|
| Published in |
BMC Bioinformatics, December 2012
|
| DOI | 10.1186/1471-2105-13-321 |
| Pubmed ID | |
| Authors |
Eithon Cadag, Peter Tarczy-Hornoch, Peter J Myler |
| Abstract |
Methods of weakening and attenuating pathogens' abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Unknown | 24 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 32% |
| Student > Bachelor | 3 | 12% |
| Professor | 2 | 8% |
| Other | 2 | 8% |
| Student > Ph. D. Student | 2 | 8% |
| Other | 5 | 20% |
| Unknown | 3 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 28% |
| Biochemistry, Genetics and Molecular Biology | 6 | 24% |
| Computer Science | 3 | 12% |
| Nursing and Health Professions | 2 | 8% |
| Medicine and Dentistry | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 3 | 12% |
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 2012.
All research outputs
#18,323,689
of 22,689,790 outputs
Outputs from BMC Bioinformatics
#6,287
of 7,252 outputs
Outputs of similar age
#215,287
of 277,382 outputs
Outputs of similar age from BMC Bioinformatics
#97
of 121 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,252 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 277,382 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.