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Timeline
X Demographics
Mendeley readers
Attention Score in Context
Title |
EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity
|
---|---|
Published in |
Journal of Chemical Information and Modeling, July 2024
|
DOI | 10.1021/acs.jcim.4c00920 |
Pubmed ID | |
Authors |
Gen Li, Ning Zhang, Xiaowen Dai, Long Fan |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Thailand | 1 | 3% |
Puerto Rico | 1 | 3% |
Unknown | 36 | 95% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 23 | 61% |
Scientists | 14 | 37% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 10 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 4 | 40% |
Unspecified | 2 | 20% |
Student > Ph. D. Student | 2 | 20% |
Student > Master | 1 | 10% |
Student > Bachelor | 1 | 10% |
Other | 0 | 0% |
Readers by discipline | Count | As % |
---|---|---|
Pharmacology, Toxicology and Pharmaceutical Science | 4 | 40% |
Biochemistry, Genetics and Molecular Biology | 3 | 30% |
Unspecified | 2 | 20% |
Energy | 1 | 10% |
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 30 August 2024.
All research outputs
#2,421,513
of 26,554,122 outputs
Outputs from Journal of Chemical Information and Modeling
#541
of 6,092 outputs
Outputs of similar age
#23,610
of 254,207 outputs
Outputs of similar age from Journal of Chemical Information and Modeling
#9
of 151 outputs
Altmetric has tracked 26,554,122 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 6,092 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done particularly well, scoring higher than 91% 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 254,207 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 90% of its contemporaries.
We're also able to compare this research output to 151 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.