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A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data

Overview of attention for article published in BioData Mining, July 2012
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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7 X users

Citations

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26 Dimensions

Readers on

mendeley
98 Mendeley
citeulike
1 CiteULike
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Title
A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data
Published in
BioData Mining, July 2012
DOI 10.1186/1756-0381-5-8
Pubmed ID
Authors

Li Li, Yang Guo, Wenwu Wu, Youyi Shi, Jian Cheng, Shiheng Tao

Abstract

Several biclustering algorithms have been proposed to identify biclusters, in which genes share similar expression patterns across a number of conditions. However, different algorithms would yield different biclusters and further lead to distinct conclusions. Therefore, some testing and comparisons between these algorithms are strongly required.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Italy 1 1%
Cuba 1 1%
Sweden 1 1%
Finland 1 1%
China 1 1%
United States 1 1%
Unknown 91 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 27%
Student > Ph. D. Student 25 26%
Student > Master 10 10%
Student > Postgraduate 6 6%
Student > Doctoral Student 5 5%
Other 18 18%
Unknown 8 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 29%
Computer Science 27 28%
Biochemistry, Genetics and Molecular Biology 11 11%
Mathematics 4 4%
Chemistry 3 3%
Other 15 15%
Unknown 10 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 25 August 2012.
All research outputs
#7,239,281
of 23,881,329 outputs
Outputs from BioData Mining
#149
of 314 outputs
Outputs of similar age
#49,937
of 166,006 outputs
Outputs of similar age from BioData Mining
#4
of 5 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 314 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 52% 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 166,006 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 69% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.