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Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics

Overview of attention for article published in BMC Bioinformatics, January 2011
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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 (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
36 Mendeley
citeulike
1 CiteULike
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Title
Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics
Published in
BMC Bioinformatics, January 2011
DOI 10.1186/1471-2105-12-303
Pubmed ID
Authors

Leonid L Chepelev, Alexandre Riazanov, Alexandre Kouznetsov, Hong Low, Michel Dumontier, Christopher JO Baker

Abstract

The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
United States 1 3%
Unknown 34 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 44%
Professor > Associate Professor 5 14%
Student > Ph. D. Student 4 11%
Student > Bachelor 3 8%
Student > Master 2 6%
Other 6 17%
Readers by discipline Count As %
Chemistry 10 28%
Agricultural and Biological Sciences 8 22%
Computer Science 7 19%
Unspecified 3 8%
Biochemistry, Genetics and Molecular Biology 3 8%
Other 5 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 17 December 2015.
All research outputs
#1,515,474
of 6,787,729 outputs
Outputs from BMC Bioinformatics
#1,164
of 3,217 outputs
Outputs of similar age
#74,802
of 286,464 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 144 outputs
Altmetric has tracked 6,787,729 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 3,217 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 62% 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 286,464 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 73% of its contemporaries.
We're also able to compare this research output to 144 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.