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Attention Score in Context
| Title |
Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints
|
|---|---|
| Published in |
BMC Bioinformatics, January 2013
|
| DOI | 10.1186/1471-2105-14-s2-s17 |
| Pubmed ID | |
| Authors |
Shaogang Ren, Bo Zeng, Xiaoning Qian |
| Abstract |
Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. |
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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 2 | 3% |
| Iran, Islamic Republic of | 1 | 1% |
| Sweden | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 63 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 26% |
| Researcher | 11 | 16% |
| Student > Master | 10 | 15% |
| Student > Doctoral Student | 7 | 10% |
| Student > Bachelor | 6 | 9% |
| Other | 10 | 15% |
| Unknown | 6 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 28% |
| Biochemistry, Genetics and Molecular Biology | 14 | 21% |
| Computer Science | 14 | 21% |
| Engineering | 8 | 12% |
| Mathematics | 1 | 1% |
| Other | 2 | 3% |
| Unknown | 10 | 15% |
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 13 March 2013.
All research outputs
#15,266,089
of 22,701,287 outputs
Outputs from BMC Bioinformatics
#5,362
of 7,254 outputs
Outputs of similar age
#179,997
of 279,318 outputs
Outputs of similar age from BMC Bioinformatics
#103
of 146 outputs
Altmetric has tracked 22,701,287 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 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 18th percentile – i.e., 18% 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 279,318 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.