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Predicting new molecular targets for rhein using network pharmacology

Overview of attention for article published in BMC Systems Biology, March 2012
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1 X user

Citations

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Title
Predicting new molecular targets for rhein using network pharmacology
Published in
BMC Systems Biology, March 2012
DOI 10.1186/1752-0509-6-20
Pubmed ID
Authors

Aihua Zhang, Hui Sun, Bo Yang, Xijun Wang

Abstract

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 1%
United States 1 1%
Russia 1 1%
China 1 1%
Unknown 65 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 26%
Student > Ph. D. Student 13 19%
Student > Master 11 16%
Student > Bachelor 5 7%
Lecturer 4 6%
Other 8 12%
Unknown 10 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 25%
Computer Science 12 17%
Medicine and Dentistry 7 10%
Biochemistry, Genetics and Molecular Biology 5 7%
Chemistry 5 7%
Other 8 12%
Unknown 15 22%
Attention Score in Context

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 21 March 2012.
All research outputs
#19,015,492
of 23,577,654 outputs
Outputs from BMC Systems Biology
#833
of 1,139 outputs
Outputs of similar age
#125,846
of 162,231 outputs
Outputs of similar age from BMC Systems Biology
#7
of 9 outputs
Altmetric has tracked 23,577,654 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 1,139 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% 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 162,231 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.