↓ Skip to main content

Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling

Overview of attention for article published in BMC Bioinformatics, January 2013
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 tweeter
peer_reviews
1 peer review site

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
42 Mendeley
Title
Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-218
Pubmed ID
Authors

Zeeshan Ahmed, Saman Zeeshan, Claudia Huber, Michael Hensel, Dietmar Schomburg, Richard Münch, Wolfgang Eisenreich, Thomas Dandekar

Abstract

The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution.

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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Brazil 1 2%
United Kingdom 1 2%
Belgium 1 2%
Russia 1 2%
Japan 1 2%
Unknown 36 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 24%
Student > Ph. D. Student 9 21%
Student > Master 5 12%
Professor 4 10%
Professor > Associate Professor 4 10%
Other 7 17%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 50%
Biochemistry, Genetics and Molecular Biology 5 12%
Medicine and Dentistry 3 7%
Engineering 2 5%
Computer Science 2 5%
Other 3 7%
Unknown 6 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 10 July 2016.
All research outputs
#6,707,265
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,504
of 4,588 outputs
Outputs of similar age
#73,961
of 192,385 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 31 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. 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 192,385 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.