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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An in silico platform for the design of heterologous pathways in nonnative metabolite production
|
|---|---|
| Published in |
BMC Bioinformatics, May 2012
|
| DOI | 10.1186/1471-2105-13-93 |
| Pubmed ID | |
| Authors |
Sunisa Chatsurachai, Chikara Furusawa, Hiroshi Shimizu |
| Abstract |
Microorganisms are used as cell factories to produce valuable compounds in pharmaceuticals, biofuels, and other industrial processes. Incorporating heterologous metabolic pathways into well-characterized hosts is a major strategy for obtaining these target metabolites and improving productivity. However, selecting appropriate heterologous metabolic pathways for a host microorganism remains difficult owing to the complexity of metabolic networks. Hence, metabolic network design could benefit greatly from the availability of an in silico platform for heterologous pathway searching. |
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 % |
|---|---|---|
| Unknown | 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 3 | 3% |
| Germany | 1 | 1% |
| Brazil | 1 | 1% |
| United Kingdom | 1 | 1% |
| Singapore | 1 | 1% |
| Thailand | 1 | 1% |
| Spain | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 82 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 30% |
| Researcher | 20 | 22% |
| Student > Master | 8 | 9% |
| Professor | 7 | 8% |
| Professor > Associate Professor | 7 | 8% |
| Other | 16 | 17% |
| Unknown | 6 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 46 | 50% |
| Biochemistry, Genetics and Molecular Biology | 14 | 15% |
| Engineering | 9 | 10% |
| Computer Science | 5 | 5% |
| Chemistry | 2 | 2% |
| Other | 5 | 5% |
| Unknown | 11 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 21 June 2022.
All research outputs
#3,214,342
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#1,161
of 7,387 outputs
Outputs of similar age
#21,393
of 165,286 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 107 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 84% 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 165,286 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.