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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Efficiently gap-filling reaction networks
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-225 |
| Pubmed ID | |
| Authors |
Mario Latendresse |
| Abstract |
Flux Balance Analysis (FBA) is a genome-scale computational technique for modeling the steady-state fluxes of an organism's reaction network. When the organism's reaction network needs to be completed to obtain growth using FBA, without relying on the genome, the completion process is called reaction gap-filling. Currently, computational techniques used to gap-fill a reaction network compute the minimum set of reactions using Mixed-Integer Linear Programming (MILP). Depending on the number of candidate reactions used to complete the model, MILP can be computationally demanding. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 40% |
| Norway | 1 | 20% |
| France | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | 1% |
| Spain | 1 | 1% |
| United States | 1 | 1% |
| Colombia | 1 | 1% |
| Unknown | 80 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 24% |
| Researcher | 13 | 15% |
| Student > Master | 9 | 11% |
| Student > Bachelor | 6 | 7% |
| Student > Doctoral Student | 4 | 5% |
| Other | 15 | 18% |
| Unknown | 17 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 21 | 25% |
| Agricultural and Biological Sciences | 19 | 23% |
| Computer Science | 7 | 8% |
| Engineering | 7 | 8% |
| Environmental Science | 2 | 2% |
| Other | 5 | 6% |
| Unknown | 23 | 27% |
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 01 July 2014.
All research outputs
#13,916,367
of 22,757,541 outputs
Outputs from BMC Bioinformatics
#4,471
of 7,272 outputs
Outputs of similar age
#116,452
of 227,594 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 153 outputs
Altmetric has tracked 22,757,541 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 7,272 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 227,594 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.