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An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models

Overview of attention for article published in PLoS Computational Biology, October 2010
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Mentioned by

patent
2 patents

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
142 Mendeley
citeulike
4 CiteULike
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Title
An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models
Published in
PLoS Computational Biology, October 2010
DOI 10.1371/journal.pcbi.1000970
Pubmed ID
Authors

Dipak Barua, Joonhoon Kim, Jennifer L. Reed

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 8%
Germany 2 1%
Singapore 1 <1%
France 1 <1%
Mexico 1 <1%
Luxembourg 1 <1%
Unknown 124 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 30%
Student > Ph. D. Student 35 25%
Student > Master 18 13%
Professor > Associate Professor 12 8%
Professor 10 7%
Other 16 11%
Unknown 9 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 47%
Engineering 16 11%
Biochemistry, Genetics and Molecular Biology 15 11%
Computer Science 12 8%
Chemical Engineering 7 5%
Other 12 8%
Unknown 13 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 April 2021.
All research outputs
#8,535,684
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#5,638
of 8,960 outputs
Outputs of similar age
#39,810
of 108,807 outputs
Outputs of similar age from PLoS Computational Biology
#36
of 63 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 33rd percentile – i.e., 33% 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 108,807 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.