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Configurable pattern-based evolutionary biclustering of gene expression data

Overview of attention for article published in Algorithms for Molecular Biology, February 2013
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1 X user

Citations

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Title
Configurable pattern-based evolutionary biclustering of gene expression data
Published in
Algorithms for Molecular Biology, February 2013
DOI 10.1186/1748-7188-8-4
Pubmed ID
Authors

Beatriz Pontes, Raúl Giráldez, Jesús S Aguilar-Ruiz

Abstract

Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Professor > Associate Professor 5 19%
Researcher 3 12%
Student > Master 3 12%
Student > Bachelor 1 4%
Other 4 15%
Unknown 4 15%
Readers by discipline Count As %
Computer Science 11 42%
Agricultural and Biological Sciences 4 15%
Unspecified 1 4%
Mathematics 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 7 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 27 February 2013.
All research outputs
#18,331,227
of 22,699,621 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#147,204
of 193,362 outputs
Outputs of similar age from Algorithms for Molecular Biology
#5
of 6 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 12th percentile – i.e., 12% 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 193,362 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.