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SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

Overview of attention for article published in BMC Bioinformatics, November 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
4 Mendeley
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Title
SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models
Published in
BMC Bioinformatics, November 2019
DOI 10.1186/s12859-019-3178-6
Pubmed ID
Authors

Xiaochuan Wang, Chen Li, Fuyi Li, Varun S. Sharma, Jiangning Song, Geoffrey I. Webb

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Researcher 1 25%
Student > Master 1 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 50%
Computer Science 1 25%
Social Sciences 1 25%

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 18 January 2020.
All research outputs
#8,098,959
of 15,456,294 outputs
Outputs from BMC Bioinformatics
#2,779
of 5,644 outputs
Outputs of similar age
#150,555
of 357,990 outputs
Outputs of similar age from BMC Bioinformatics
#277
of 592 outputs
Altmetric has tracked 15,456,294 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,644 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 50% 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 357,990 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 57% of its contemporaries.
We're also able to compare this research output to 592 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 53% of its contemporaries.