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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

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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7 X users
googleplus
1 Google+ user

Citations

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44 Dimensions

Readers on

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106 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 106 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 102 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 17%
Student > Ph. D. Student 15 14%
Student > Master 14 13%
Professor > Associate Professor 9 8%
Student > Doctoral Student 7 7%
Other 29 27%
Unknown 14 13%
Readers by discipline Count As %
Computer Science 27 25%
Agricultural and Biological Sciences 20 19%
Medicine and Dentistry 12 11%
Pharmacology, Toxicology and Pharmaceutical Science 9 8%
Biochemistry, Genetics and Molecular Biology 4 4%
Other 14 13%
Unknown 20 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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
#6,335,546
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#2,280
of 7,454 outputs
Outputs of similar age
#66,596
of 288,089 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 136 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 69% 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 288,089 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 76% of its contemporaries.
We're also able to compare this research output to 136 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 69% of its contemporaries.