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Computational mass spectrometry for small molecules

Overview of attention for article published in Journal of Cheminformatics, March 2013
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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 (86th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

twitter
6 X users
patent
1 patent
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
131 Dimensions

Readers on

mendeley
245 Mendeley
citeulike
2 CiteULike
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Title
Computational mass spectrometry for small molecules
Published in
Journal of Cheminformatics, March 2013
DOI 10.1186/1758-2946-5-12
Pubmed ID
Authors

Kerstin Scheubert, Franziska Hufsky, Sebastian Böcker

Abstract

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 3 1%
United Kingdom 3 1%
Germany 2 <1%
United States 2 <1%
France 1 <1%
Australia 1 <1%
Colombia 1 <1%
Portugal 1 <1%
Czechia 1 <1%
Other 6 2%
Unknown 224 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 72 29%
Researcher 51 21%
Student > Master 22 9%
Student > Bachelor 19 8%
Professor > Associate Professor 12 5%
Other 35 14%
Unknown 34 14%
Readers by discipline Count As %
Chemistry 71 29%
Agricultural and Biological Sciences 53 22%
Biochemistry, Genetics and Molecular Biology 23 9%
Computer Science 15 6%
Engineering 9 4%
Other 34 14%
Unknown 40 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 29 January 2024.
All research outputs
#3,379,727
of 25,252,667 outputs
Outputs from Journal of Cheminformatics
#314
of 953 outputs
Outputs of similar age
#26,489
of 200,162 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 17 outputs
Altmetric has tracked 25,252,667 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 953 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has gotten more attention than average, scoring higher than 66% 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 200,162 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 86% of its contemporaries.
We're also able to compare this research output to 17 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 64% of its contemporaries.