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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting PDZ domain mediated protein interactions from structure
|
|---|---|
| Published in |
BMC Bioinformatics, January 2013
|
| DOI | 10.1186/1471-2105-14-27 |
| Pubmed ID | |
| Authors |
Shirley Hui, Xiang Xing, Gary D Bader |
| Abstract |
PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 17% |
| Japan | 1 | 17% |
| Norway | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Israel | 1 | 1% |
| India | 1 | 1% |
| Canada | 1 | 1% |
| Spain | 1 | 1% |
| Japan | 1 | 1% |
| Unknown | 71 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 20 | 26% |
| Student > Ph. D. Student | 18 | 24% |
| Student > Bachelor | 11 | 14% |
| Professor > Associate Professor | 6 | 8% |
| Student > Master | 6 | 8% |
| Other | 11 | 14% |
| Unknown | 4 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 39% |
| Biochemistry, Genetics and Molecular Biology | 22 | 29% |
| Medicine and Dentistry | 5 | 7% |
| Computer Science | 4 | 5% |
| Engineering | 4 | 5% |
| Other | 8 | 11% |
| Unknown | 3 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 28 September 2017.
All research outputs
#7,345,736
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#2,761
of 7,454 outputs
Outputs of similar age
#78,549
of 284,347 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 144 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,454 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 61% 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 284,347 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 144 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 57% of its contemporaries.