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SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data

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

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
30 Mendeley
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Title
SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data
Published in
BMC Bioinformatics, October 2019
DOI 10.1186/s12859-019-3067-z
Pubmed ID
Authors

Yuntong Li, Teresa W.M. Fan, Andrew N. Lane, Woo-Young Kang, Susanne M. Arnold, Arnold J. Stromberg, Chi Wang, Li Chen

Twitter Demographics

The data shown below were collected from the profiles of 8 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 23%
Student > Ph. D. Student 6 20%
Unspecified 4 13%
Professor 3 10%
Student > Bachelor 3 10%
Other 5 17%
Unknown 2 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 23%
Unspecified 5 17%
Computer Science 3 10%
Agricultural and Biological Sciences 2 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 4 13%
Unknown 8 27%

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 28 November 2019.
All research outputs
#6,860,440
of 21,168,082 outputs
Outputs from BMC Bioinformatics
#2,844
of 6,881 outputs
Outputs of similar age
#144,068
of 351,465 outputs
Outputs of similar age from BMC Bioinformatics
#268
of 596 outputs
Altmetric has tracked 21,168,082 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,881 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 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 351,465 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 55% of its contemporaries.
We're also able to compare this research output to 596 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 52% of its contemporaries.