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Partial inhibition and bilevel optimization in flux balance analysis

Overview of attention for article published in BMC Bioinformatics, November 2013
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1 tweeter

Citations

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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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 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 37 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 34%
Student > Ph. D. Student 9 22%
Student > Doctoral Student 3 7%
Professor 3 7%
Student > Postgraduate 2 5%
Other 7 17%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 32%
Biochemistry, Genetics and Molecular Biology 8 20%
Computer Science 7 17%
Environmental Science 1 2%
Mathematics 1 2%
Other 4 10%
Unknown 7 17%

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
#11,550,225
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#4,492
of 5,420 outputs
Outputs of similar age
#176,109
of 254,988 outputs
Outputs of similar age from BMC Bioinformatics
#347
of 416 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 8th percentile – i.e., 8% 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 254,988 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 416 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.