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How to get the most from microarray data: advice from reverse genomics

Overview of attention for article published in BMC Genomics, March 2014
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
1 news outlet
twitter
5 X users
weibo
1 weibo user

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
29 Mendeley
citeulike
4 CiteULike
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Title
How to get the most from microarray data: advice from reverse genomics
Published in
BMC Genomics, March 2014
DOI 10.1186/1471-2164-15-223
Pubmed ID
Authors

Ivan P Gorlov, Ji-Yeon Yang, Jinyoung Byun, Christopher Logothetis, Olga Y Gorlova, Kim-Anh Do, Christopher Amos

Abstract

Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data-derived predictor of known cancer associated genes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
China 1 3%
Switzerland 1 3%
Unknown 26 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 34%
Student > Ph. D. Student 7 24%
Professor 3 10%
Student > Master 3 10%
Student > Bachelor 2 7%
Other 3 10%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 38%
Medicine and Dentistry 5 17%
Computer Science 4 14%
Biochemistry, Genetics and Molecular Biology 3 10%
Mathematics 1 3%
Other 2 7%
Unknown 3 10%
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 12 May 2014.
All research outputs
#2,655,540
of 25,373,627 outputs
Outputs from BMC Genomics
#759
of 11,244 outputs
Outputs of similar age
#26,004
of 237,291 outputs
Outputs of similar age from BMC Genomics
#14
of 210 outputs
Altmetric has tracked 25,373,627 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 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 93% 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 237,291 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 88% of its contemporaries.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.