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Structure-based classification and ontology in chemistry

Overview of attention for article published in Journal of Cheminformatics, April 2012
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
2 blogs
twitter
10 X users
facebook
1 Facebook page

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
98 Mendeley
citeulike
5 CiteULike
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Title
Structure-based classification and ontology in chemistry
Published in
Journal of Cheminformatics, April 2012
DOI 10.1186/1758-2946-4-8
Pubmed ID
Authors

Janna Hastings, Despoina Magka, Colin Batchelor, Lian Duan, Robert Stevens, Marcus Ennis, Christoph Steinbeck

Abstract

Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Portugal 1 1%
Germany 1 1%
France 1 1%
Italy 1 1%
Brazil 1 1%
Netherlands 1 1%
United Kingdom 1 1%
India 1 1%
Other 2 2%
Unknown 86 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 31%
Student > Ph. D. Student 22 22%
Student > Master 11 11%
Student > Postgraduate 5 5%
Other 5 5%
Other 11 11%
Unknown 14 14%
Readers by discipline Count As %
Computer Science 23 23%
Agricultural and Biological Sciences 19 19%
Chemistry 17 17%
Biochemistry, Genetics and Molecular Biology 6 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Other 11 11%
Unknown 19 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 04 April 2022.
All research outputs
#1,662,286
of 23,479,361 outputs
Outputs from Journal of Cheminformatics
#143
of 868 outputs
Outputs of similar age
#9,781
of 162,838 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 9 outputs
Altmetric has tracked 23,479,361 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 868 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 83% 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 162,838 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them