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Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models

Overview of attention for article published in BMC Bioinformatics, April 2021
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
20 Mendeley
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Title
Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models
Published in
BMC Bioinformatics, April 2021
DOI 10.1186/s12859-021-04015-9
Pubmed ID
Authors

Beatriz Galindo-Prieto, Paul Geladi, Johan Trygg

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 30%
Student > Doctoral Student 3 15%
Other 2 10%
Student > Bachelor 2 10%
Professor > Associate Professor 2 10%
Other 3 15%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 4 20%
Agricultural and Biological Sciences 3 15%
Business, Management and Accounting 2 10%
Medicine and Dentistry 2 10%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 3 15%
Unknown 5 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 10 April 2021.
All research outputs
#6,502,816
of 23,310,485 outputs
Outputs from BMC Bioinformatics
#2,494
of 7,382 outputs
Outputs of similar age
#143,204
of 432,105 outputs
Outputs of similar age from BMC Bioinformatics
#65
of 168 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 7,382 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 66% 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 432,105 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 66% of its contemporaries.
We're also able to compare this research output to 168 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 61% of its contemporaries.