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On the selection of appropriate distances for gene expression data clustering

Overview of attention for article published in BMC Bioinformatics, January 2014
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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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
66 Dimensions

Readers on

mendeley
170 Mendeley
citeulike
2 CiteULike
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Title
On the selection of appropriate distances for gene expression data clustering
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-s2-s2
Pubmed ID
Authors

Pablo A Jaskowiak, Ricardo JGB Campello, Ivan G Costa

Abstract

Clustering is crucial for gene expression data analysis. As an unsupervised exploratory procedure its results can help researchers to gain insights and formulate new hypothesis about biological data from microarrays. Given different settings of microarray experiments, clustering proves itself as a versatile exploratory tool. It can help to unveil new cancer subtypes or to identify groups of genes that respond similarly to a specific experimental condition. In order to obtain useful clustering results, however, different parameters of the clustering procedure must be properly tuned. Besides the selection of the clustering method itself, determining which distance is going to be employed between data objects is probably one of the most difficult decisions.

Mendeley readers

The data shown below were compiled from readership statistics for 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
United Kingdom 3 2%
Belgium 1 <1%
Argentina 1 <1%
Nigeria 1 <1%
China 1 <1%
Russia 1 <1%
Brazil 1 <1%
Unknown 156 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 54 32%
Researcher 35 21%
Student > Master 27 16%
Student > Bachelor 17 10%
Student > Doctoral Student 10 6%
Other 14 8%
Unknown 13 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 31%
Biochemistry, Genetics and Molecular Biology 32 19%
Computer Science 31 18%
Engineering 10 6%
Medicine and Dentistry 7 4%
Other 20 12%
Unknown 18 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 10 May 2020.
All research outputs
#1,800,358
of 15,220,640 outputs
Outputs from BMC Bioinformatics
#721
of 5,579 outputs
Outputs of similar age
#36,727
of 286,066 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 41 outputs
Altmetric has tracked 15,220,640 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,579 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 86% 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 286,066 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 86% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.