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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Drug repositioning: a machine-learning approach through data integration
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2013
|
| DOI | 10.1186/1758-2946-5-30 |
| Pubmed ID | |
| Authors |
Francesco Napolitano, Yan Zhao, Vânia M Moreira, Roberto Tagliaferri, Juha Kere, Mauro D’Amato, Dario Greco |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 17% |
| Germany | 1 | 17% |
| United States | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Scientists | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 379 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 1% |
| Brazil | 3 | <1% |
| Spain | 2 | <1% |
| United Kingdom | 2 | <1% |
| France | 1 | <1% |
| Finland | 1 | <1% |
| Germany | 1 | <1% |
| Portugal | 1 | <1% |
| Netherlands | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 361 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 78 | 21% |
| Student > Master | 59 | 16% |
| Researcher | 57 | 15% |
| Student > Bachelor | 25 | 7% |
| Other | 19 | 5% |
| Other | 57 | 15% |
| Unknown | 84 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 65 | 17% |
| Biochemistry, Genetics and Molecular Biology | 51 | 13% |
| Agricultural and Biological Sciences | 44 | 12% |
| Pharmacology, Toxicology and Pharmaceutical Science | 28 | 7% |
| Chemistry | 27 | 7% |
| Other | 59 | 16% |
| Unknown | 105 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. 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 January 2024.
All research outputs
#1,704,029
of 26,017,215 outputs
Outputs from Journal of Cheminformatics
#116
of 984 outputs
Outputs of similar age
#14,084
of 213,538 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 13 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 984 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 88% of its peers.
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 213,538 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 13 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 69% of its contemporaries.