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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Protein complex detection using interaction reliability assessment and weighted clustering coefficient
|
|---|---|
| Published in |
BMC Bioinformatics, May 2013
|
| DOI | 10.1186/1471-2105-14-163 |
| Pubmed ID | |
| Authors |
Nazar Zaki, Dmitry Efimov, Jose Berengueres |
| Abstract |
Predicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes. |
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 % |
|---|---|---|
| United States | 1 | 50% |
| Norway | 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 4% |
| Iran, Islamic Republic of | 1 | 2% |
| Unknown | 53 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 32% |
| Researcher | 6 | 11% |
| Professor | 5 | 9% |
| Professor > Associate Professor | 5 | 9% |
| Student > Master | 4 | 7% |
| Other | 9 | 16% |
| Unknown | 9 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 25% |
| Computer Science | 11 | 20% |
| Biochemistry, Genetics and Molecular Biology | 10 | 18% |
| Engineering | 4 | 7% |
| Mathematics | 2 | 4% |
| Other | 3 | 5% |
| Unknown | 12 | 21% |
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 20 May 2013.
All research outputs
#17,689,426
of 22,711,242 outputs
Outputs from BMC Bioinformatics
#5,919
of 7,259 outputs
Outputs of similar age
#140,354
of 195,768 outputs
Outputs of similar age from BMC Bioinformatics
#102
of 127 outputs
Altmetric has tracked 22,711,242 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 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 13th percentile – i.e., 13% 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 195,768 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.