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Discovering context-specific relationships from biological literature by using multi-level context terms

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2012
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1 tweeter

Citations

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Title
Discovering context-specific relationships from biological literature by using multi-level context terms
Published in
BMC Medical Informatics and Decision Making, April 2012
DOI 10.1186/1472-6947-12-s1-s1
Pubmed ID
Authors

Sejoon Lee, Jaejoon Choi, Kyunghyun Park, Min Song, Doheon Lee

Abstract

The Swanson's ABC model is powerful to infer hidden relationships buried in biological literature. However, the model is inadequate to infer relations with context information. In addition, the model generates a very large amount of candidates from biological text, and it is a semi-automatic, labor-intensive technique requiring human expert's manual input. To tackle these problems, we incorporate context terms to infer relations between AB interactions and BC interactions.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 24%
Researcher 6 21%
Student > Master 5 17%
Student > Bachelor 2 7%
Other 2 7%
Other 4 14%
Unknown 3 10%
Readers by discipline Count As %
Computer Science 6 21%
Agricultural and Biological Sciences 4 14%
Psychology 4 14%
Neuroscience 3 10%
Medicine and Dentistry 3 10%
Other 3 10%
Unknown 6 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 19 May 2012.
All research outputs
#9,934,096
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#927
of 1,122 outputs
Outputs of similar age
#83,764
of 118,031 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 10 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 2nd percentile – i.e., 2% 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 118,031 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.