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MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

Overview of attention for article published in BMC Bioinformatics, October 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

blogs
1 blog
googleplus
1 Google+ user

Citations

dimensions_citation
77 Dimensions

Readers on

mendeley
110 Mendeley
citeulike
1 CiteULike
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Title
MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures
Published in
BMC Bioinformatics, October 2011
DOI 10.1186/1471-2105-12-400
Pubmed ID
Authors

Dan Tulpan, Serge Léger, Luc Belliveau, Adrian Culf, Miroslava Čuperlović-Culf

Abstract

One-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 4%
Colombia 2 2%
Switzerland 2 2%
Brazil 2 2%
Vietnam 1 <1%
Ethiopia 1 <1%
India 1 <1%
Germany 1 <1%
Estonia 1 <1%
Other 2 2%
Unknown 93 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 24%
Researcher 25 23%
Student > Master 14 13%
Student > Doctoral Student 11 10%
Professor > Associate Professor 6 5%
Other 22 20%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 33%
Chemistry 22 20%
Biochemistry, Genetics and Molecular Biology 15 14%
Computer Science 7 6%
Engineering 6 5%
Other 10 9%
Unknown 14 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 25 May 2014.
All research outputs
#2,808,676
of 14,783,970 outputs
Outputs from BMC Bioinformatics
#1,242
of 5,489 outputs
Outputs of similar age
#20,773
of 111,569 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 176 outputs
Altmetric has tracked 14,783,970 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,489 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 77% of its peers.
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 111,569 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 176 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.