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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Industry-scale application and evaluation of deep learning for drug target prediction
|
|---|---|
| Published in |
Journal of Cheminformatics, April 2020
|
| DOI | 10.1186/s13321-020-00428-5 |
| Pubmed ID | |
| Authors |
Noé Sturm, Andreas Mayr, Thanh Le Van, Vladimir Chupakhin, Hugo Ceulemans, Joerg Wegner, Jose-Felipe Golib-Dzib, Nina Jeliazkova, Yves Vandriessche, Stanislav Böhm, Vojtech Cima, Jan Martinovic, Nigel Greene, Tom Vander Aa, Thomas J. Ashby, Sepp Hochreiter, Ola Engkvist, Günter Klambauer, Hongming Chen |
X Demographics
The data shown below were collected from the profiles of 30 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 3 | 10% |
| India | 2 | 7% |
| United Kingdom | 2 | 7% |
| France | 2 | 7% |
| United States | 2 | 7% |
| Australia | 1 | 3% |
| Germany | 1 | 3% |
| Brazil | 1 | 3% |
| Japan | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 14 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 19 | 63% |
| Scientists | 10 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 124 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 17% |
| Researcher | 19 | 15% |
| Student > Master | 13 | 10% |
| Other | 7 | 6% |
| Student > Doctoral Student | 5 | 4% |
| Other | 15 | 12% |
| Unknown | 44 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 16 | 13% |
| Biochemistry, Genetics and Molecular Biology | 8 | 6% |
| Computer Science | 8 | 6% |
| Engineering | 7 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 5% |
| Other | 32 | 26% |
| Unknown | 47 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 13 January 2021.
All research outputs
#2,189,894
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#187
of 934 outputs
Outputs of similar age
#53,042
of 378,433 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 25 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 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 80% 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 378,433 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.