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Overview of attention for article published in BMC Bioinformatics, January 2006
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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 (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

twitter
2 tweeters
patent
3 patents

Citations

dimensions_citation
1319 Dimensions

Readers on

mendeley
1052 Mendeley
citeulike
23 CiteULike
connotea
3 Connotea
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Title
Published in
BMC Bioinformatics, January 2006
DOI 10.1186/1471-2105-7-3
Pubmed ID
Authors

Ramón Díaz-Uriarte, Sara Alvarez de Andrés

Abstract

Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1,052 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 24 2%
United Kingdom 9 <1%
Australia 7 <1%
Brazil 7 <1%
India 7 <1%
Canada 6 <1%
Spain 6 <1%
Germany 5 <1%
Italy 4 <1%
Other 34 3%
Unknown 943 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 316 30%
Researcher 202 19%
Student > Master 153 15%
Unspecified 77 7%
Student > Bachelor 62 6%
Other 242 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 234 22%
Computer Science 233 22%
Unspecified 131 12%
Engineering 81 8%
Biochemistry, Genetics and Molecular Biology 68 6%
Other 305 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 28 September 2017.
All research outputs
#1,737,671
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#750
of 4,576 outputs
Outputs of similar age
#13,247
of 86,507 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 35 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 83% 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 86,507 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 84% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.