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Mendeley readers
Attention Score in Context
| Title |
Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction
|
|---|---|
| Published in |
BMC Bioinformatics, September 2012
|
| DOI | 10.1186/1471-2105-13-235 |
| Pubmed ID | |
| Authors |
Elin Teppa, Angela D Wilkins, Morten Nielsen, Cristina Marino Buslje |
| Abstract |
A large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap. |
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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 3% |
| Brazil | 1 | 2% |
| Germany | 1 | 2% |
| Czechia | 1 | 2% |
| Spain | 1 | 2% |
| Unknown | 58 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 27% |
| Researcher | 13 | 20% |
| Student > Bachelor | 6 | 9% |
| Student > Master | 5 | 8% |
| Professor > Associate Professor | 4 | 6% |
| Other | 11 | 17% |
| Unknown | 8 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 38% |
| Biochemistry, Genetics and Molecular Biology | 16 | 25% |
| Computer Science | 7 | 11% |
| Unspecified | 2 | 3% |
| Physics and Astronomy | 2 | 3% |
| Other | 3 | 5% |
| Unknown | 10 | 16% |
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 14 September 2012.
All research outputs
#15,251,053
of 22,678,224 outputs
Outputs from BMC Bioinformatics
#5,361
of 7,249 outputs
Outputs of similar age
#106,293
of 168,685 outputs
Outputs of similar age from BMC Bioinformatics
#58
of 91 outputs
Altmetric has tracked 22,678,224 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,249 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 18th percentile – i.e., 18% 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 168,685 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.