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XMetDB: an open access database for xenobiotic metabolism

Overview of attention for article published in Journal of Cheminformatics, September 2016
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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1 blog
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21 X users
googleplus
2 Google+ users

Citations

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10 Dimensions

Readers on

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58 Mendeley
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2 CiteULike
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Title
XMetDB: an open access database for xenobiotic metabolism
Published in
Journal of Cheminformatics, September 2016
DOI 10.1186/s13321-016-0161-3
Pubmed ID
Authors

Ola Spjuth, Patrik Rydberg, Egon L. Willighagen, Chris T. Evelo, Nina Jeliazkova

Abstract

Xenobiotic metabolism is an active research topic but the limited amount of openly available high-quality biotransformation data constrains predictive modeling. Current database often default to commonly available information: which enzyme metabolizes a compound, but neither experimental conditions nor the atoms that undergo metabolization are captured. We present XMetDB, an open access database for drugs and other xenobiotics and their respective metabolites. The database contains chemical structures of xenobiotic biotransformations with substrate atoms annotated as reaction centra, the resulting product formed, and the catalyzing enzyme, type of experiment, and literature references. Associated with the database is a web interface for the submission and retrieval of experimental metabolite data for drugs and other xenobiotics in various formats, and a web API for programmatic access is also available. The database is open for data deposition, and a curation scheme is in place for quality control. An extensive guide on how to enter experimental data into is available from the XMetDB wiki. XMetDB formalizes how biotransformation data should be reported, and the openly available systematically labeled data is a big step forward towards better models for predictive metabolism.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 3%
Bulgaria 1 2%
Brazil 1 2%
Unknown 54 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 22%
Researcher 13 22%
Student > Master 8 14%
Student > Bachelor 5 9%
Other 4 7%
Other 12 21%
Unknown 3 5%
Readers by discipline Count As %
Chemistry 11 19%
Biochemistry, Genetics and Molecular Biology 9 16%
Computer Science 8 14%
Agricultural and Biological Sciences 7 12%
Engineering 5 9%
Other 12 21%
Unknown 6 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 03 February 2017.
All research outputs
#1,664,207
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#120
of 934 outputs
Outputs of similar age
#28,744
of 327,906 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 20 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 87% 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 327,906 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 91% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.