↓ Skip to main content

Self-organizing ontology of biochemically relevant small molecules

Overview of attention for article published in BMC Bioinformatics, January 2012
Altmetric Badge

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 (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
71 Mendeley
citeulike
12 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Self-organizing ontology of biochemically relevant small molecules
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-3
Pubmed ID
Authors

Leonid L Chepelev, Janna Hastings, Marcus Ennis, Christoph Steinbeck, Michel Dumontier

Abstract

The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 10%
Brazil 1 1%
India 1 1%
China 1 1%
Mexico 1 1%
Japan 1 1%
Russia 1 1%
Unknown 58 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 41%
Student > Ph. D. Student 15 21%
Other 7 10%
Student > Master 6 8%
Professor > Associate Professor 4 6%
Other 9 13%
Unknown 1 1%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 35%
Computer Science 18 25%
Chemistry 6 8%
Biochemistry, Genetics and Molecular Biology 5 7%
Immunology and Microbiology 3 4%
Other 10 14%
Unknown 4 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 10 August 2020.
All research outputs
#3,125,233
of 18,639,770 outputs
Outputs from BMC Bioinformatics
#1,260
of 6,406 outputs
Outputs of similar age
#34,236
of 234,196 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 353 outputs
Altmetric has tracked 18,639,770 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,406 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 80% 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 234,196 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 85% of its contemporaries.
We're also able to compare this research output to 353 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.