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X Demographics
Mendeley readers
Attention Score in Context
| Title |
In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2014
|
| DOI | 10.1186/1758-2946-6-33 |
| Pubmed ID | |
| Authors |
Xian Liu, Yuan Xu, Shanshan Li, Yulan Wang, Jianlong Peng, Cheng Luo, Xiaomin Luo, Mingyue Zheng, Kaixian Chen, Hualiang Jiang |
| Abstract |
Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound. |
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 % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Germany | 2 | 2% |
| Brazil | 2 | 2% |
| Netherlands | 1 | <1% |
| Italy | 1 | <1% |
| Japan | 1 | <1% |
| Spain | 1 | <1% |
| Unknown | 106 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 29 | 25% |
| Student > Ph. D. Student | 24 | 21% |
| Student > Master | 15 | 13% |
| Professor > Associate Professor | 9 | 8% |
| Professor | 7 | 6% |
| Other | 18 | 15% |
| Unknown | 15 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 17% |
| Chemistry | 20 | 17% |
| Computer Science | 16 | 14% |
| Biochemistry, Genetics and Molecular Biology | 11 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 5% |
| Other | 18 | 15% |
| Unknown | 26 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 16 October 2014.
All research outputs
#14,782,026
of 22,757,541 outputs
Outputs from Journal of Cheminformatics
#733
of 828 outputs
Outputs of similar age
#127,411
of 228,273 outputs
Outputs of similar age from Journal of Cheminformatics
#14
of 15 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 10th percentile – i.e., 10% 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 228,273 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.