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Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

Overview of attention for article published in BMC Systems Biology, December 2013
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2 X users

Citations

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5 Dimensions

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74 Mendeley
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Title
Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways
Published in
BMC Systems Biology, December 2013
DOI 10.1186/1752-0509-7-134
Pubmed ID
Authors

Jon Pey, Kaspar Valgepea, Angel Rubio, John E Beasley, Francisco J Planes

Abstract

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Uruguay 1 1%
Brazil 1 1%
Sweden 1 1%
India 1 1%
Australia 1 1%
Iran, Islamic Republic of 1 1%
Singapore 1 1%
Estonia 1 1%
Other 1 1%
Unknown 63 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 28%
Student > Ph. D. Student 18 24%
Student > Master 10 14%
Professor > Associate Professor 5 7%
Professor 4 5%
Other 9 12%
Unknown 7 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 45%
Computer Science 11 15%
Biochemistry, Genetics and Molecular Biology 10 14%
Engineering 3 4%
Environmental Science 1 1%
Other 7 9%
Unknown 9 12%
Attention Score in Context

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 09 December 2013.
All research outputs
#15,286,644
of 22,733,113 outputs
Outputs from BMC Systems Biology
#644
of 1,142 outputs
Outputs of similar age
#192,307
of 306,761 outputs
Outputs of similar age from BMC Systems Biology
#33
of 59 outputs
Altmetric has tracked 22,733,113 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 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 32nd percentile – i.e., 32% 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 306,761 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.