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MAAMD: a workflow to standardize meta-analyses and comparison of affymetrix microarray data

Overview of attention for article published in BMC Bioinformatics, March 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
37 Mendeley
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Title
MAAMD: a workflow to standardize meta-analyses and comparison of affymetrix microarray data
Published in
BMC Bioinformatics, March 2014
DOI 10.1186/1471-2105-15-69
Pubmed ID
Authors

Zhuohui Gan, Jianwu Wang, Nathan Salomonis, Jennifer C Stowe, Gabriel G Haddad, Andrew D McCulloch, Ilkay Altintas, Alexander C Zambon

Abstract

Mandatory deposit of raw microarray data files for public access, prior to study publication, provides significant opportunities to conduct new bioinformatics analyses within and across multiple datasets. Analysis of raw microarray data files (e.g. Affymetrix CEL files) can be time consuming, complex, and requires fundamental computational and bioinformatics skills. The development of analytical workflows to automate these tasks simplifies the processing of, improves the efficiency of, and serves to standardize multiple and sequential analyses. Once installed, workflows facilitate the tedious steps required to run rapid intra- and inter-dataset comparisons.

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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 3%
Sweden 1 3%
United Kingdom 1 3%
Singapore 1 3%
Argentina 1 3%
Unknown 32 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 27%
Researcher 10 27%
Student > Bachelor 3 8%
Professor 3 8%
Librarian 2 5%
Other 5 14%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 35%
Biochemistry, Genetics and Molecular Biology 6 16%
Medicine and Dentistry 5 14%
Computer Science 4 11%
Engineering 2 5%
Other 2 5%
Unknown 5 14%

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 20 March 2014.
All research outputs
#3,637,190
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,524
of 5,420 outputs
Outputs of similar age
#44,140
of 188,919 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 4 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. 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 188,919 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 76% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.