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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Steiner tree methods for optimal sub-network identification: an empirical study
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-144 |
| Pubmed ID | |
| Authors |
Afshin Sadeghi, Holger Fröhlich |
| Abstract |
Analysis and interpretation of biological networks is one of the primary goals of systems biology. In this context identification of sub-networks connecting sets of seed proteins or seed genes plays a crucial role. Given that no natural node and edge weighting scheme is available retrieval of a minimum size sub-graph leads to the classical Steiner tree problem, which is known to be NP-complete. Many approximate solutions have been published and theoretically analyzed in the computer science literature, but far less is known about their practical performance in the bioinformatics field. |
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% |
| Norway | 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 1% |
| Netherlands | 1 | 1% |
| France | 1 | 1% |
| United Kingdom | 1 | 1% |
| Iran, Islamic Republic of | 1 | 1% |
| Slovenia | 1 | 1% |
| Unknown | 61 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 28% |
| Researcher | 19 | 28% |
| Student > Master | 5 | 7% |
| Other | 4 | 6% |
| Student > Doctoral Student | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 7 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 25% |
| Computer Science | 14 | 21% |
| Mathematics | 8 | 12% |
| Biochemistry, Genetics and Molecular Biology | 7 | 10% |
| Engineering | 5 | 7% |
| Other | 4 | 6% |
| Unknown | 12 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 05 December 2017.
All research outputs
#13,888,916
of 22,708,120 outputs
Outputs from BMC Bioinformatics
#4,469
of 7,256 outputs
Outputs of similar age
#105,942
of 192,344 outputs
Outputs of similar age from BMC Bioinformatics
#78
of 122 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,256 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 35th percentile – i.e., 35% 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 192,344 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.