↓ Skip to main content

A novel feature-based approach to extract drug–drug interactions from biomedical text

Overview of attention for article published in Bioinformatics, August 2014
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

Mentioned by

twitter
6 X users

Citations

dimensions_citation
68 Dimensions

Readers on

mendeley
106 Mendeley
citeulike
1 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
A novel feature-based approach to extract drug–drug interactions from biomedical text
Published in
Bioinformatics, August 2014
DOI 10.1093/bioinformatics/btu557
Pubmed ID
Authors

Quoc-Chinh Bui, Peter M A Sloot, Erik M van Mulligen, Jan A Kors

Abstract

Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 %
Netherlands 2 2%
France 1 <1%
New Caledonia 1 <1%
China 1 <1%
Spain 1 <1%
United States 1 <1%
Croatia 1 <1%
Unknown 98 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 17%
Researcher 18 17%
Student > Master 13 12%
Other 9 8%
Student > Doctoral Student 7 7%
Other 21 20%
Unknown 20 19%
Readers by discipline Count As %
Computer Science 37 35%
Agricultural and Biological Sciences 9 8%
Biochemistry, Genetics and Molecular Biology 7 7%
Medicine and Dentistry 7 7%
Engineering 4 4%
Other 14 13%
Unknown 28 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 2015.
All research outputs
#14,600,553
of 25,374,647 outputs
Outputs from Bioinformatics
#8,581
of 12,808 outputs
Outputs of similar age
#117,262
of 246,923 outputs
Outputs of similar age from Bioinformatics
#156
of 228 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 246,923 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 228 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.