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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational prediction of protein-protein complexes
|
|---|---|
| Published in |
BMC Research Notes, September 2012
|
| DOI | 10.1186/1756-0500-5-495 |
| Pubmed ID | |
| Authors |
Seema Mishra |
| Abstract |
Protein-protein interactions form the core of several biological processes. With protein-protein interfaces being considered as drug targets, studies on their interactions and molecular mechanisms are gaining ground. As the number of protein complexes in databases is scarce as compared to a spectrum of independent protein molecules, computational approaches are being considered for speedier model derivation and assessment of a plausible complex. In this study, a good approach towards in silico generation of protein-protein heterocomplex and identification of the most probable complex among thousands of complexes thus generated is documented. This approach becomes even more useful in the event of little or no binding site information between the interacting protein molecules. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
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 % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Japan | 1 | 3% |
| Australia | 1 | 3% |
| Unknown | 29 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 34% |
| Researcher | 4 | 13% |
| Student > Postgraduate | 4 | 13% |
| Student > Master | 3 | 9% |
| Other | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 4 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 34% |
| Biochemistry, Genetics and Molecular Biology | 8 | 25% |
| Computer Science | 2 | 6% |
| Medicine and Dentistry | 2 | 6% |
| Psychology | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 6 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 27 June 2021.
All research outputs
#14,171,441
of 22,712,476 outputs
Outputs from BMC Research Notes
#1,948
of 4,257 outputs
Outputs of similar age
#97,908
of 168,850 outputs
Outputs of similar age from BMC Research Notes
#44
of 91 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,257 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 168,850 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.