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X Demographics
Mendeley readers
Attention Score in Context
| Title |
mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines
|
|---|---|
| Published in |
BMC Bioinformatics, November 2012
|
| DOI | 10.1186/1471-2105-13-290 |
| Pubmed ID | |
| Authors |
Shibiao Wan, Man-Wai Mak, Sun-Yuan Kung |
| Abstract |
Although many computational methods have been developed to predict protein subcellular localization, most of the methods are limited to the prediction of single-location proteins. Multi-location proteins are either not considered or assumed not existing. However, proteins with multiple locations are particularly interesting because they may have special biological functions, which are essential to both basic research and drug discovery. |
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% |
| Peru | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 2% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 50 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 26% |
| Student > Master | 10 | 19% |
| Student > Ph. D. Student | 6 | 11% |
| Professor > Associate Professor | 3 | 6% |
| Professor | 2 | 4% |
| Other | 7 | 13% |
| Unknown | 11 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 25% |
| Agricultural and Biological Sciences | 12 | 23% |
| Biochemistry, Genetics and Molecular Biology | 7 | 13% |
| Decision Sciences | 1 | 2% |
| Medicine and Dentistry | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 17 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 08 November 2012.
All research outputs
#12,864,199
of 22,685,926 outputs
Outputs from BMC Bioinformatics
#3,780
of 7,253 outputs
Outputs of similar age
#95,797
of 183,491 outputs
Outputs of similar age from BMC Bioinformatics
#58
of 111 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,253 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 45th percentile – i.e., 45% 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 183,491 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 111 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.