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BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology

Overview of attention for article published in BMC Bioinformatics, February 2015
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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 (82nd percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
47 Mendeley
citeulike
3 CiteULike
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Title
BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0486-3
Pubmed ID
Authors

Pablo Moreno, Stephan Beisken, Bhavana Harsha, Venkatesh Muthukrishnan, Ilinca Tudose, Adriano Dekker, Stefanie Dornfeldt, Franziska Taruttis, Ivo Grosse, Janna Hastings, Steffen Neumann, Christoph Steinbeck

Abstract

Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis. We describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology. BiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 2%
South Africa 1 2%
Brazil 1 2%
Unknown 44 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 40%
Student > Ph. D. Student 10 21%
Student > Master 5 11%
Student > Postgraduate 3 6%
Student > Bachelor 2 4%
Other 3 6%
Unknown 5 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 40%
Biochemistry, Genetics and Molecular Biology 9 19%
Computer Science 5 11%
Chemistry 2 4%
Medicine and Dentistry 2 4%
Other 4 9%
Unknown 6 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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
#2,894,478
of 18,639,770 outputs
Outputs from BMC Bioinformatics
#1,112
of 6,406 outputs
Outputs of similar age
#38,837
of 225,076 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 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 84th 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 82% 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 225,076 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 82% of its contemporaries.
We're also able to compare this research output to 1 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