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Mendeley readers
Attention Score in Context
| Title |
Clustering protein environments for function prediction: finding PROSITE motifs in 3D
|
|---|---|
| Published in |
BMC Bioinformatics, May 2007
|
| DOI | 10.1186/1471-2105-8-s4-s10 |
| Pubmed ID | |
| Authors |
Sungroh Yoon, Jessica C Ebert, Eui-Young Chung, Giovanni De Micheli, Russ B Altman |
| Abstract |
Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified. |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 13% |
| Korea, Republic of | 1 | 3% |
| Unknown | 26 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 39% |
| Student > Ph. D. Student | 8 | 26% |
| Professor > Associate Professor | 3 | 10% |
| Professor | 2 | 6% |
| Student > Master | 2 | 6% |
| Other | 1 | 3% |
| Unknown | 3 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 29% |
| Computer Science | 9 | 29% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Medicine and Dentistry | 2 | 6% |
| Engineering | 2 | 6% |
| Other | 3 | 10% |
| Unknown | 3 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 January 2009.
All research outputs
#5,444,628
of 22,707,247 outputs
Outputs from BMC Bioinformatics
#1,974
of 7,255 outputs
Outputs of similar age
#19,908
of 70,769 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 47 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. Compared to these this one has done well and is in the 76th 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 gotten more attention than average, scoring higher than 72% 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 70,769 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 71% of its contemporaries.
We're also able to compare this research output to 47 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 59% of its contemporaries.