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DupChecker: a bioconductor package for checking high-throughput genomic data redundancy in meta-analysis

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

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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9 X users
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1 Google+ user

Citations

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3 Dimensions

Readers on

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27 Mendeley
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Title
DupChecker: a bioconductor package for checking high-throughput genomic data redundancy in meta-analysis
Published in
BMC Bioinformatics, September 2014
DOI 10.1186/1471-2105-15-323
Pubmed ID
Authors

Quanhu Sheng, Yu Shyr, Xi Chen

Abstract

Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates could lead false positive finding, misleading clustering pattern or model over-fitting issue, etc in the subsequent data analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 4%
United States 1 4%
Denmark 1 4%
Unknown 24 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 30%
Student > Ph. D. Student 6 22%
Student > Master 3 11%
Student > Bachelor 2 7%
Professor 1 4%
Other 3 11%
Unknown 4 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 26%
Computer Science 6 22%
Biochemistry, Genetics and Molecular Biology 4 15%
Medicine and Dentistry 4 15%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 1 4%
Unknown 4 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 November 2014.
All research outputs
#5,430,426
of 22,764,165 outputs
Outputs from BMC Bioinformatics
#1,943
of 7,273 outputs
Outputs of similar age
#56,909
of 252,706 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 107 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,273 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 73% 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 252,706 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 77% of its contemporaries.
We're also able to compare this research output to 107 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 72% of its contemporaries.