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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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Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
73 Mendeley
citeulike
5 CiteULike
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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

Pey J, Valgepea K, Rubio A, Beasley JE, Planes FJ, 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Philippines 1 1%
Chile 1 1%
Australia 1 1%
Brazil 1 1%
India 1 1%
Estonia 1 1%
Uruguay 1 1%
Iran, Islamic Republic of 1 1%
Other 2 3%
Unknown 61 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 29%
Student > Ph. D. Student 18 25%
Student > Master 10 14%
Student > Postgraduate 4 5%
Unspecified 4 5%
Other 16 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 49%
Biochemistry, Genetics and Molecular Biology 11 15%
Computer Science 10 14%
Unspecified 7 10%
Engineering 3 4%
Other 6 8%

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
#2,313,256
of 4,506,977 outputs
Outputs from BMC Systems Biology
#314
of 670 outputs
Outputs of similar age
#59,224
of 122,569 outputs
Outputs of similar age from BMC Systems Biology
#22
of 38 outputs
Altmetric has tracked 4,506,977 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 670 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 42nd percentile – i.e., 42% 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 122,569 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.