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A method to identify differential expression profiles of time-course gene data with Fourier transformation

Overview of attention for article published in BMC Bioinformatics, October 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
64 Mendeley
citeulike
1 CiteULike
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Title
A method to identify differential expression profiles of time-course gene data with Fourier transformation
Published in
BMC Bioinformatics, October 2013
DOI 10.1186/1471-2105-14-310
Pubmed ID
Authors

Jaehee Kim, Robert Todd Ogden, Haseong Kim

Abstract

Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
India 1 2%
Germany 1 2%
Unknown 61 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 38%
Student > Ph. D. Student 22 34%
Professor > Associate Professor 4 6%
Professor 3 5%
Student > Master 3 5%
Other 7 11%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 53%
Biochemistry, Genetics and Molecular Biology 9 14%
Computer Science 5 8%
Engineering 3 5%
Medicine and Dentistry 2 3%
Other 6 9%
Unknown 5 8%

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 23 October 2013.
All research outputs
#5,941,780
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,221
of 4,195 outputs
Outputs of similar age
#58,243
of 153,584 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 96 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
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 153,584 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 61% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.