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An integrated pharmacokinetics ontology and corpus for text mining

Overview of attention for article published in BMC Bioinformatics, February 2013
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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 (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
7 tweeters
googleplus
1 Google+ user

Citations

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33 Dimensions

Readers on

mendeley
98 Mendeley
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Title
An integrated pharmacokinetics ontology and corpus for text mining
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-35
Pubmed ID
Authors

Heng-Yi Wu, Shreyas Karnik, Abhinita Subhadarshini, Zhiping Wang, Santosh Philips, Xu Han, Chienwei Chiang, Lei Liu, Malaz Boustani, Luis M Rocha, Sara K Quinney, David Flockhart, Lang Li

Abstract

Drug pharmacokinetics parameters, drug interaction parameters, and pharmacogenetics data have been unevenly collected in different databases and published extensively in the literature. Without appropriate pharmacokinetics ontology and a well annotated pharmacokinetics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection from the literature and pharmacokinetics data integration from multiple databases.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
India 1 1%
Netherlands 1 1%
Unknown 94 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 18%
Student > Ph. D. Student 15 15%
Student > Master 14 14%
Professor > Associate Professor 9 9%
Student > Doctoral Student 7 7%
Other 27 28%
Unknown 8 8%
Readers by discipline Count As %
Computer Science 26 27%
Agricultural and Biological Sciences 20 20%
Medicine and Dentistry 11 11%
Pharmacology, Toxicology and Pharmaceutical Science 9 9%
Biochemistry, Genetics and Molecular Biology 5 5%
Other 13 13%
Unknown 14 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 March 2013.
All research outputs
#3,318,553
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,333
of 5,420 outputs
Outputs of similar age
#49,121
of 243,741 outputs
Outputs of similar age from BMC Bioinformatics
#51
of 189 outputs
Altmetric has tracked 14,573,111 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 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 75% 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 243,741 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 79% of its contemporaries.
We're also able to compare this research output to 189 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 71% of its contemporaries.