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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Target network differences between western drugs and Chinese herbal ingredients in treating cardiovascular disease
|
|---|---|
| Published in |
BMC Bioinformatics, March 2014
|
| DOI | 10.1186/1471-2105-15-s4-s3 |
| Pubmed ID | |
| Authors |
Peng Fu, Linlin Yang, Yi Sun, Li Ye, Zhiwei Cao, Kailin Tang |
| Abstract |
Western drugs have achieved great successes in CVDs treatment. However, they may lead to some side effects and drug resistance. On the other hand, more and more studies found that Traditional Chinese herbs have efficient therapeutic effects for CVDs, while their therapeutic mechanism is still not very clear. It may be a good view towards molecules, targets and network to decipher whether difference exists between anti-CVD western drugs and Chinese herbal ingredients. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 2 | 15% |
| Professor > Associate Professor | 2 | 15% |
| Lecturer > Senior Lecturer | 1 | 8% |
| Student > Ph. D. Student | 1 | 8% |
| Other | 1 | 8% |
| Other | 2 | 15% |
| Unknown | 4 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 23% |
| Computer Science | 2 | 15% |
| Psychology | 1 | 8% |
| Medicine and Dentistry | 1 | 8% |
| Chemistry | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 5 | 38% |
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 07 November 2014.
All research outputs
#20,242,136
of 22,769,322 outputs
Outputs from BMC Bioinformatics
#6,845
of 7,273 outputs
Outputs of similar age
#191,578
of 223,424 outputs
Outputs of similar age from BMC Bioinformatics
#86
of 95 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% 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 223,424 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.