↓ Skip to main content

baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data

Overview of attention for article published in BMC Bioinformatics, August 2010
Altmetric Badge

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 (80th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
2 X users
patent
15 patents
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
666 Dimensions

Readers on

mendeley
1040 Mendeley
citeulike
24 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Published in
BMC Bioinformatics, August 2010
DOI 10.1186/1471-2105-11-422
Pubmed ID
Authors

Thomas J Hardcastle, Krystyna A Kelly

Abstract

High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key way of analysing such data is to look for elements of the data which display particular patterns of differential expression in order to take these forward for further analysis and validation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 1,040 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 27 3%
United Kingdom 15 1%
Germany 10 <1%
Brazil 5 <1%
France 4 <1%
Sweden 4 <1%
Canada 4 <1%
Norway 3 <1%
Spain 3 <1%
Other 24 2%
Unknown 941 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 288 28%
Researcher 255 25%
Student > Master 121 12%
Student > Bachelor 61 6%
Professor > Associate Professor 52 5%
Other 162 16%
Unknown 101 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 488 47%
Biochemistry, Genetics and Molecular Biology 191 18%
Computer Science 55 5%
Mathematics 51 5%
Medicine and Dentistry 31 3%
Other 101 10%
Unknown 123 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 December 2023.
All research outputs
#4,180,565
of 22,831,537 outputs
Outputs from BMC Bioinformatics
#1,609
of 7,288 outputs
Outputs of similar age
#17,295
of 94,611 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 52 outputs
Altmetric has tracked 22,831,537 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,288 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 done well, scoring higher than 77% 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 94,611 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 80% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.