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Rule-based multi-scale simulation for drug effect pathway analysis

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2013
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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 (88th percentile)

Mentioned by

blogs
2 blogs

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
32 Mendeley
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Title
Rule-based multi-scale simulation for drug effect pathway analysis
Published in
BMC Medical Informatics and Decision Making, January 2013
DOI 10.1186/1472-6947-13-s1-s4
Pubmed ID
Authors

Woochang Hwang, Yongdeuk Hwang, Sunjae Lee, Doheon Lee

Abstract

Biological systems are robust and complex to maintain stable phenotypes under various conditions. In these systems, drugs reported the limited efficacy and unexpected side-effects. To remedy this situation, many pharmaceutical laboratories have begun to research combination drugs and some of them have shown successful clinical results. Complementary action of multiple compounds could increase efficacy as well as reduce side-effects through pharmacological interactions. However, experimental approach requires vast cost of preclinical experiments and tests as the number of possible combinations of compound dosages increases exponentially. Computer model-based experiments have been emerging as one of the most promising solutions to cope with such complexity. Though there have been many efforts to model specific molecular pathways using qualitative and quantitative formalisms, they suffer from unexpected results caused by distant interactions beyond their localized models.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
United States 1 3%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 34%
Student > Ph. D. Student 8 25%
Student > Doctoral Student 5 16%
Student > Postgraduate 2 6%
Professor 1 3%
Other 4 13%
Unknown 1 3%
Readers by discipline Count As %
Medicine and Dentistry 8 25%
Biochemistry, Genetics and Molecular Biology 6 19%
Computer Science 5 16%
Engineering 3 9%
Agricultural and Biological Sciences 3 9%
Other 4 13%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 08 July 2013.
All research outputs
#1,905,253
of 17,351,915 outputs
Outputs from BMC Medical Informatics and Decision Making
#154
of 1,572 outputs
Outputs of similar age
#18,793
of 163,713 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 17,351,915 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,572 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 90% 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 163,713 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 88% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them