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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

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
3 X users
peer_reviews
1 peer review site

Citations

dimensions_citation
2996 Dimensions

Readers on

mendeley
575 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users 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 575 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 570 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 71 12%
Student > Master 56 10%
Unspecified 49 9%
Researcher 42 7%
Student > Bachelor 42 7%
Other 86 15%
Unknown 229 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 66 11%
Pharmacology, Toxicology and Pharmaceutical Science 51 9%
Unspecified 50 9%
Medicine and Dentistry 43 7%
Agricultural and Biological Sciences 42 7%
Other 81 14%
Unknown 242 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 October 2014.
All research outputs
#8,031,401
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#630
of 891 outputs
Outputs of similar age
#68,496
of 207,534 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 27 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 27th percentile – i.e., 27% 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 207,534 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 53% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.