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X Demographics
Mendeley readers
Attention Score in Context
| Title |
PURE: A webserver for the prediction of domains in unassigned regions in proteins
|
|---|---|
| Published in |
BMC Bioinformatics, June 2008
|
| DOI | 10.1186/1471-2105-9-281 |
| Pubmed ID | |
| Authors |
Chilamakuri CS Reddy, Khader Shameer, Bernard O Offmann, Ramanathan Sowdhamini |
| Abstract |
Protein domains are the structural and functional units of proteins. The ability to parse proteins into different domains is important for effective classification, understanding of protein structure, function, and evolution and is hence biologically relevant. Several computational methods are available to identify domains in the sequence. Domain finding algorithms often employ stringent thresholds to recognize sequence domains. Identification of additional domains can be tedious involving intense computation and manual intervention but can lead to better understanding of overall biological function. In this context, the problem of identifying new domains in the unassigned regions of a protein sequence assumes a crucial importance. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| India | 1 | 33% |
| United States | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 3% |
| Poland | 1 | 3% |
| Germany | 1 | 3% |
| Australia | 1 | 3% |
| Unknown | 31 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 31% |
| Researcher | 8 | 23% |
| Professor | 5 | 14% |
| Student > Master | 4 | 11% |
| Student > Bachelor | 2 | 6% |
| Other | 3 | 9% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 49% |
| Biochemistry, Genetics and Molecular Biology | 6 | 17% |
| Computer Science | 3 | 9% |
| Medicine and Dentistry | 3 | 9% |
| Chemistry | 2 | 6% |
| Other | 1 | 3% |
| Unknown | 3 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 23 July 2021.
All research outputs
#3,670,029
of 22,705,019 outputs
Outputs from BMC Bioinformatics
#1,387
of 7,255 outputs
Outputs of similar age
#12,533
of 81,908 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 38 outputs
Altmetric has tracked 22,705,019 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,255 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 done well, scoring higher than 80% 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 81,908 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.