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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
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
8 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
90 Dimensions

Readers on

mendeley
198 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.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 198 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
Russia 3 2%
Germany 2 1%
Switzerland 2 1%
United States 2 1%
Finland 1 <1%
South Africa 1 <1%
Australia 1 <1%
Czechia 1 <1%
Other 6 3%
Unknown 176 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 33%
Researcher 48 24%
Student > Master 16 8%
Student > Bachelor 14 7%
Professor > Associate Professor 12 6%
Other 29 15%
Unknown 13 7%
Readers by discipline Count As %
Chemistry 57 29%
Agricultural and Biological Sciences 49 25%
Biochemistry, Genetics and Molecular Biology 19 10%
Computer Science 14 7%
Chemical Engineering 8 4%
Other 32 16%
Unknown 19 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 March 2018.
All research outputs
#2,869,209
of 12,620,777 outputs
Outputs from Journal of Cheminformatics
#250
of 506 outputs
Outputs of similar age
#30,450
of 143,326 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 8 outputs
Altmetric has tracked 12,620,777 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 506 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has gotten more attention than average, scoring higher than 50% 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 143,326 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 78% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.