↓ Skip to main content

ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data

Overview of attention for article published in BMC Bioinformatics, November 2019
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
12 X users

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
63 Mendeley
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.
Title
ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data
Published in
BMC Bioinformatics, November 2019
DOI 10.1186/s12859-019-3144-3
Pubmed ID
Authors

Matúš Medo, Daniel M. Aebersold, Michaela Medová

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 27%
Researcher 14 22%
Student > Bachelor 8 13%
Student > Master 5 8%
Student > Doctoral Student 3 5%
Other 3 5%
Unknown 13 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 32%
Agricultural and Biological Sciences 7 11%
Neuroscience 4 6%
Computer Science 3 5%
Medicine and Dentistry 3 5%
Other 10 16%
Unknown 16 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 14 August 2020.
All research outputs
#2,602,653
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#789
of 7,454 outputs
Outputs of similar age
#56,581
of 366,895 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 195 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 89% 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 366,895 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 84% of its contemporaries.
We're also able to compare this research output to 195 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.