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Timeline
X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction
|
---|---|
Chapter number | 15 |
Book title |
Computational Methods for Predicting Post-Translational Modification Sites
|
Published in |
Methods in molecular biology, June 2022
|
DOI | 10.1007/978-1-0716-2317-6_15 |
Pubmed ID | |
Book ISBNs |
978-1-07-162316-9, 978-1-07-162317-6
|
Authors |
Pakhrin, Subash C., Pokharel, Suresh, Saigo, Hiroto, KC, Dukka B. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 25% |
Australia | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 6 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 2 | 33% |
Unspecified | 1 | 17% |
Researcher | 1 | 17% |
Professor > Associate Professor | 1 | 17% |
Unknown | 1 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Unspecified | 1 | 17% |
Biochemistry, Genetics and Molecular Biology | 1 | 17% |
Computer Science | 1 | 17% |
Immunology and Microbiology | 1 | 17% |
Engineering | 1 | 17% |
Other | 0 | 0% |
Unknown | 1 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 June 2022.
All research outputs
#13,182,656
of 22,769,322 outputs
Outputs from Methods in molecular biology
#3,450
of 13,090 outputs
Outputs of similar age
#173,405
of 438,306 outputs
Outputs of similar age from Methods in molecular biology
#90
of 559 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,090 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 72% 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 438,306 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 559 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.