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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Sequence-based prediction of protein-protein interactions by means of codon usage
|
|---|---|
| Published in |
Genome Biology, May 2008
|
| DOI | 10.1186/gb-2008-9-5-r87 |
| Pubmed ID | |
| Authors |
Hamed Shateri Najafabadi, Reza Salavati |
| Abstract |
We introduce a novel approach to predict interaction of two proteins solely by analyzing their coding sequences. We found that similarity in codon usage is a strong predictor of protein-protein interactions and, for high specificity values, is as sensitive as the most powerful current prediction methods. Furthermore, combining codon usage with other predictors results in a 75% increase in sensitivity at a precision of 50%, compared to prediction without considering codon usage. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 6% |
| Germany | 4 | 5% |
| Japan | 3 | 3% |
| United Kingdom | 2 | 2% |
| Italy | 2 | 2% |
| India | 1 | 1% |
| Brazil | 1 | 1% |
| Belgium | 1 | 1% |
| Austria | 1 | 1% |
| Other | 2 | 2% |
| Unknown | 65 | 75% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 32% |
| Student > Ph. D. Student | 20 | 23% |
| Student > Master | 12 | 14% |
| Other | 5 | 6% |
| Student > Bachelor | 4 | 5% |
| Other | 12 | 14% |
| Unknown | 6 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 52 | 60% |
| Biochemistry, Genetics and Molecular Biology | 11 | 13% |
| Computer Science | 9 | 10% |
| Business, Management and Accounting | 2 | 2% |
| Social Sciences | 2 | 2% |
| Other | 4 | 5% |
| Unknown | 7 | 8% |
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 26 January 2021.
All research outputs
#17,285,036
of 25,371,288 outputs
Outputs from Genome Biology
#4,093
of 4,467 outputs
Outputs of similar age
#83,834
of 97,836 outputs
Outputs of similar age from Genome Biology
#27
of 35 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 5th percentile – i.e., 5% 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 97,836 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.