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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A proposed syntax for Minimotif Semantics, version 1
|
|---|---|
| Published in |
BMC Genomics, August 2009
|
| DOI | 10.1186/1471-2164-10-360 |
| Pubmed ID | |
| Authors |
Jay Vyas, Ronald J Nowling, Mark W Maciejewski, Sanguthevar Rajasekaran, Michael R Gryk, Martin R Schiller |
| Abstract |
One of the most important developments in bioinformatics over the past few decades has been the observation that short linear peptide sequences (minimotifs) mediate many classes of cellular functions such as protein-protein interactions, molecular trafficking and post-translational modifications. As both the creators and curators of a database which catalogues minimotifs, Minimotif Miner, the authors have a unique perspective on the commonalities of the many functional roles of minimotifs. There is an obvious usefulness in standardizing functional annotations both in allowing for the facile exchange of data between various bioinformatics resources, as well as the internal clustering of sets of related data elements. With these two purposes in mind, the authors provide a proposed syntax for minimotif semantics primarily useful for functional annotation. |
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 % |
|---|---|---|
| Unknown | 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 8% |
| United States | 1 | 8% |
| Unknown | 10 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 50% |
| Researcher | 2 | 17% |
| Professor | 1 | 8% |
| Student > Master | 1 | 8% |
| Unknown | 2 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 42% |
| Agricultural and Biological Sciences | 4 | 33% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Unknown | 2 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 02 April 2020.
All research outputs
#6,414,688
of 22,790,780 outputs
Outputs from BMC Genomics
#2,879
of 10,647 outputs
Outputs of similar age
#32,813
of 110,956 outputs
Outputs of similar age from BMC Genomics
#12
of 31 outputs
Altmetric has tracked 22,790,780 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 10,647 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 71% 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 110,956 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.