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Mendeley readers
Attention Score in Context
| Title |
A structure filter for the Eukaryotic Linear Motif Resource
|
|---|---|
| Published in |
BMC Bioinformatics, October 2009
|
| DOI | 10.1186/1471-2105-10-351 |
| Pubmed ID | |
| Authors |
Allegra Via, Cathryn M Gould, Christine Gemünd, Toby J Gibson, Manuela Helmer-Citterich |
| Abstract |
Many proteins are highly modular, being assembled from globular domains and segments of natively disordered polypeptides. Linear motifs, short sequence modules functioning independently of protein tertiary structure, are most abundant in natively disordered polypeptides but are also found in accessible parts of globular domains, such as exposed loops. The prediction of novel occurrences of known linear motifs attempts the difficult task of distinguishing functional matches from stochastically occurring non-functional matches. Although functionality can only be confirmed experimentally, confidence in a putative motif is increased if a motif exhibits attributes associated with functional instances such as occurrence in the correct taxonomic range, cellular compartment, conservation in homologues and accessibility to interacting partners. Several tools now use these attributes to classify putative motifs based on confidence of functionality. |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 3% |
| Germany | 1 | 3% |
| Australia | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 29 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 30% |
| Student > Ph. D. Student | 9 | 27% |
| Student > Master | 4 | 12% |
| Student > Bachelor | 4 | 12% |
| Professor > Associate Professor | 3 | 9% |
| Other | 2 | 6% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 64% |
| Computer Science | 4 | 12% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Chemical Engineering | 1 | 3% |
| Immunology and Microbiology | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 1 | 3% |
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 09 September 2011.
All research outputs
#7,454,298
of 22,789,076 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#33,830
of 94,171 outputs
Outputs of similar age from BMC Bioinformatics
#28
of 62 outputs
Altmetric has tracked 22,789,076 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 94,171 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 62 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.