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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Partial inhibition and bilevel optimization in flux balance analysis
|
|---|---|
| Published in |
BMC Bioinformatics, November 2013
|
| DOI | 10.1186/1471-2105-14-344 |
| Pubmed ID | |
| Authors |
Giuseppe Facchetti, Claudio Altafini |
| Abstract |
Within Flux Balance Analysis, the investigation of complex subtasks, such as finding the optimal perturbation of the network or finding an optimal combination of drugs, often requires to set up a bilevel optimization problem. In order to keep the linearity and convexity of these nested optimization problems, an ON/OFF description of the effect of the perturbation (i.e. Boolean variable) is normally used. This restriction may not be realistic when one wants, for instance, to describe the partial inhibition of a reaction induced by a drug. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | 2% |
| India | 1 | 2% |
| Singapore | 1 | 2% |
| Belgium | 1 | 2% |
| Unknown | 39 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 33% |
| Student > Ph. D. Student | 10 | 23% |
| Professor | 3 | 7% |
| Student > Doctoral Student | 3 | 7% |
| Student > Master | 3 | 7% |
| Other | 7 | 16% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 33% |
| Biochemistry, Genetics and Molecular Biology | 8 | 19% |
| Computer Science | 8 | 19% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Mathematics | 1 | 2% |
| Other | 4 | 9% |
| Unknown | 7 | 16% |
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 29 November 2013.
All research outputs
#20,211,690
of 22,733,113 outputs
Outputs from BMC Bioinformatics
#6,838
of 7,266 outputs
Outputs of similar age
#267,417
of 306,996 outputs
Outputs of similar age from BMC Bioinformatics
#95
of 105 outputs
Altmetric has tracked 22,733,113 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,266 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 1st percentile – i.e., 1% 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 306,996 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.