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A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data

Overview of attention for article published in BMC Bioinformatics, May 2009
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

patent
1 patent

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
31 Mendeley
citeulike
1 CiteULike
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Title
A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data
Published in
BMC Bioinformatics, May 2009
DOI 10.1186/1471-2105-10-165
Pubmed ID
Authors

Xiaofeng Dai, Timo Erkkilä, Olli Yli-Harja, Harri Lähdesmäki

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 6%
Hong Kong 1 3%
Brazil 1 3%
Finland 1 3%
United Kingdom 1 3%
Unknown 25 81%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 29%
Researcher 6 19%
Professor > Associate Professor 4 13%
Student > Master 3 10%
Other 2 6%
Other 5 16%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 32%
Computer Science 5 16%
Engineering 4 13%
Mathematics 2 6%
Decision Sciences 2 6%
Other 5 16%
Unknown 3 10%
Attention Score in Context

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 20 May 2014.
All research outputs
#7,552,525
of 23,039,416 outputs
Outputs from BMC Bioinformatics
#3,041
of 7,318 outputs
Outputs of similar age
#38,711
of 114,812 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 36 outputs
Altmetric has tracked 23,039,416 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,318 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 50% 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 114,812 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.