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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 (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
5 tweeters
peer_reviews
1 peer review site

Citations

dimensions_citation
420 Dimensions

Readers on

mendeley
194 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 194 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 189 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 18%
Student > Master 32 16%
Researcher 23 12%
Student > Bachelor 15 8%
Student > Postgraduate 12 6%
Other 38 20%
Unknown 39 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 16%
Agricultural and Biological Sciences 27 14%
Medicine and Dentistry 27 14%
Pharmacology, Toxicology and Pharmaceutical Science 21 11%
Computer Science 17 9%
Other 26 13%
Unknown 45 23%

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
#2,951,312
of 11,350,565 outputs
Outputs from Journal of Cheminformatics
#259
of 444 outputs
Outputs of similar age
#45,556
of 176,163 outputs
Outputs of similar age from Journal of Cheminformatics
#5
of 26 outputs
Altmetric has tracked 11,350,565 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 444 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.7. This one is in the 41st percentile – i.e., 41% 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 176,163 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 73% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.