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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS
|
|---|---|
| Published in |
BMC Genomics, September 2013
|
| DOI | 10.1186/1471-2164-14-608 |
| Pubmed ID | |
| Authors |
Eli Rodgers-Melnick, Mark Culp, Stephen P DiFazio |
| Abstract |
The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 33% |
| Japan | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 3% |
| United States | 1 | 2% |
| Germany | 1 | 2% |
| India | 1 | 2% |
| Unknown | 55 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 27% |
| Student > Ph. D. Student | 15 | 25% |
| Student > Postgraduate | 6 | 10% |
| Student > Doctoral Student | 5 | 8% |
| Professor | 4 | 7% |
| Other | 12 | 20% |
| Unknown | 2 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 40% |
| Biochemistry, Genetics and Molecular Biology | 18 | 30% |
| Computer Science | 6 | 10% |
| Engineering | 2 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Other | 5 | 8% |
| Unknown | 4 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 06 April 2018.
All research outputs
#5,972,875
of 22,721,584 outputs
Outputs from BMC Genomics
#2,483
of 10,626 outputs
Outputs of similar age
#51,404
of 198,346 outputs
Outputs of similar age from BMC Genomics
#29
of 145 outputs
Altmetric has tracked 22,721,584 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 10,626 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 76% 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 198,346 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 73% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.