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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improved multi-level protein–protein interaction prediction with semantic-based regularization
|
|---|---|
| Published in |
BMC Bioinformatics, April 2014
|
| DOI | 10.1186/1471-2105-15-103 |
| Pubmed ID | |
| Authors |
Claudio Saccà, Stefano Teso, Michelangelo Diligenti, Andrea Passerini |
| Abstract |
Protein-protein interactions can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn form interfaces through patches of residues. Detailed knowledge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the binding process and more efficient drug/enzyme design. Alas, most current interaction prediction methods do not identify which parts of a protein actually instantiate an interaction. Furthermore, they also fail to leverage the hierarchical nature of the problem, ignoring otherwise useful information available at the lower levels; when they do, they do not generate predictions that are guaranteed to be consistent between levels. |
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 % |
|---|---|---|
| Norway | 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Philippines | 1 | 3% |
| Unknown | 38 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 20% |
| Researcher | 7 | 18% |
| Student > Doctoral Student | 4 | 10% |
| Student > Bachelor | 4 | 10% |
| Professor | 4 | 10% |
| Other | 9 | 23% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 33% |
| Agricultural and Biological Sciences | 9 | 23% |
| Biochemistry, Genetics and Molecular Biology | 3 | 8% |
| Chemistry | 2 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 5 | 13% |
| Unknown | 7 | 18% |
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 12 April 2014.
All research outputs
#18,370,767
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#6,302
of 7,269 outputs
Outputs of similar age
#164,393
of 226,854 outputs
Outputs of similar age from BMC Bioinformatics
#91
of 115 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,269 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 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 226,854 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.