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X Demographics
Mendeley readers
Attention Score in Context
| Title |
EnzML: multi-label prediction of enzyme classes using InterPro signatures
|
|---|---|
| Published in |
BMC Bioinformatics, April 2012
|
| DOI | 10.1186/1471-2105-13-61 |
| Pubmed ID | |
| Authors |
Luna De Ferrari, Stuart Aitken, Jano van Hemert, Igor Goryanin |
| Abstract |
Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 2% |
| United States | 2 | 2% |
| Brazil | 1 | 1% |
| India | 1 | 1% |
| Mexico | 1 | 1% |
| Norway | 1 | 1% |
| Spain | 1 | 1% |
| Thailand | 1 | 1% |
| Unknown | 83 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 19% |
| Student > Ph. D. Student | 14 | 15% |
| Student > Master | 12 | 13% |
| Student > Bachelor | 10 | 11% |
| Professor > Associate Professor | 8 | 9% |
| Other | 24 | 26% |
| Unknown | 7 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 31 | 33% |
| Computer Science | 20 | 22% |
| Biochemistry, Genetics and Molecular Biology | 13 | 14% |
| Engineering | 5 | 5% |
| Medicine and Dentistry | 5 | 5% |
| Other | 8 | 9% |
| Unknown | 11 | 12% |
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 28 September 2012.
All research outputs
#14,431,072
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#4,561
of 7,400 outputs
Outputs of similar age
#96,569
of 164,868 outputs
Outputs of similar age from BMC Bioinformatics
#65
of 102 outputs
Altmetric has tracked 23,577,654 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 7,400 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 35th percentile – i.e., 35% 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 164,868 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.