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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
|
|---|---|
| Published in |
BMC Bioinformatics, September 2014
|
| DOI | 10.1186/1471-2105-15-303 |
| Pubmed ID | |
| Authors |
Prashant Shingate, Malini Manoharan, Anshul Sukhwal, Ramanathan Sowdhamini |
| Abstract |
Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Korea, Republic of | 1 | 3% |
| United Kingdom | 1 | 3% |
| Spain | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 28 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 44% |
| Researcher | 6 | 19% |
| Student > Bachelor | 4 | 13% |
| Student > Doctoral Student | 2 | 6% |
| Professor | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 38% |
| Agricultural and Biological Sciences | 7 | 22% |
| Chemistry | 5 | 16% |
| Computer Science | 3 | 9% |
| Environmental Science | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 3 | 9% |
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 16 September 2014.
All research outputs
#20,236,620
of 22,763,032 outputs
Outputs from BMC Bioinformatics
#6,845
of 7,273 outputs
Outputs of similar age
#188,345
of 225,899 outputs
Outputs of similar age from BMC Bioinformatics
#104
of 113 outputs
Altmetric has tracked 22,763,032 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 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 1st percentile – i.e., 1% 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 225,899 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.