You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
Title |
Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles
|
---|---|
Published in |
Journal of Cheminformatics, March 2022
|
DOI | 10.1186/s13321-022-00596-6 |
Pubmed ID | |
Authors |
Chengyou Liu, Andrew M. Hogan, Hunter Sturm, Mohd Wasif Khan, Md. Mohaiminul Islam, A. S. M. Zisanur Rahman, Rebecca Davis, Silvia T. Cardona, Pingzhao Hu |
X Demographics
The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 4 | 15% |
United States | 3 | 12% |
Japan | 3 | 12% |
Thailand | 1 | 4% |
Argentina | 1 | 4% |
India | 1 | 4% |
Brazil | 1 | 4% |
Sweden | 1 | 4% |
Unknown | 11 | 42% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 13 | 50% |
Members of the public | 12 | 46% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 44 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 6 | 14% |
Researcher | 5 | 11% |
Student > Bachelor | 4 | 9% |
Professor | 3 | 7% |
Other | 3 | 7% |
Other | 9 | 20% |
Unknown | 14 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 7 | 16% |
Biochemistry, Genetics and Molecular Biology | 3 | 7% |
Agricultural and Biological Sciences | 3 | 7% |
Social Sciences | 3 | 7% |
Computer Science | 3 | 7% |
Other | 11 | 25% |
Unknown | 14 | 32% |
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 31 March 2022.
All research outputs
#2,248,488
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#192
of 934 outputs
Outputs of similar age
#51,724
of 434,521 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 21 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 90th 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 79% 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 434,521 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 88% of its contemporaries.
We're also able to compare this research output to 21 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 71% of its contemporaries.