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TCMSP: a database of systems pharmacology for drug discovery from herbal medicines

Overview of attention for article published in Journal of Cheminformatics, April 2014
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About this Attention Score

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

Mentioned by

twitter
5 tweeters
peer_reviews
1 peer review site

Citations

dimensions_citation
1567 Dimensions

Readers on

mendeley
373 Mendeley
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Title
TCMSP: a database of systems pharmacology for drug discovery from herbal medicines
Published in
Journal of Cheminformatics, April 2014
DOI 10.1186/1758-2946-6-13
Pubmed ID
Authors

Jinlong Ru, Peng Li, Jinan Wang, Wei Zhou, Bohui Li, Chao Huang, Pidong Li, Zihu Guo, Weiyang Tao, Yinfeng Yang, Xue Xu, Yan Li, Yonghua Wang, Ling Yang

Abstract

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters 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 373 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 2 <1%
Netherlands 1 <1%
United States 1 <1%
Brunei Darussalam 1 <1%
Unknown 368 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 56 15%
Student > Master 45 12%
Researcher 36 10%
Student > Bachelor 29 8%
Student > Postgraduate 22 6%
Other 55 15%
Unknown 130 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 56 15%
Medicine and Dentistry 41 11%
Pharmacology, Toxicology and Pharmaceutical Science 36 10%
Agricultural and Biological Sciences 33 9%
Computer Science 21 6%
Other 44 12%
Unknown 142 38%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 August 2015.
All research outputs
#5,477,065
of 17,988,068 outputs
Outputs from Journal of Cheminformatics
#456
of 689 outputs
Outputs of similar age
#45,816
of 158,191 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 17,988,068 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 689 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one is in the 33rd percentile – i.e., 33% 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 158,191 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 69% 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