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Mendeley readers
Attention Score in Context
| Title |
Improving metabolic flux predictions using absolute gene expression data
|
|---|---|
| Published in |
BMC Systems Biology, June 2012
|
| DOI | 10.1186/1752-0509-6-73 |
| Pubmed ID | |
| Authors |
Dave Lee, Kieran Smallbone, Warwick B Dunn, Ettore Murabito, Catherine L Winder, Douglas B Kell, Pedro Mendes, Neil Swainston |
| Abstract |
Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se. |
Mendeley readers
The data shown below were compiled from readership statistics for 293 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 2% |
| United Kingdom | 3 | 1% |
| Sweden | 3 | 1% |
| Netherlands | 2 | <1% |
| Germany | 2 | <1% |
| Iran, Islamic Republic of | 2 | <1% |
| Hungary | 1 | <1% |
| Singapore | 1 | <1% |
| Thailand | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 271 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 73 | 25% |
| Researcher | 67 | 23% |
| Student > Master | 47 | 16% |
| Other | 19 | 6% |
| Student > Doctoral Student | 13 | 4% |
| Other | 48 | 16% |
| Unknown | 26 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 113 | 39% |
| Biochemistry, Genetics and Molecular Biology | 52 | 18% |
| Computer Science | 38 | 13% |
| Engineering | 21 | 7% |
| Chemical Engineering | 11 | 4% |
| Other | 20 | 7% |
| Unknown | 38 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 17. 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 11 April 2015.
All research outputs
#1,922,247
of 23,577,654 outputs
Outputs from BMC Systems Biology
#36
of 1,139 outputs
Outputs of similar age
#11,977
of 165,717 outputs
Outputs of similar age from BMC Systems Biology
#1
of 38 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,139 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 96% 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 165,717 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.