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Gene selection and classification of microarray data using random forest

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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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2 X users
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8 patents

Citations

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2159 Dimensions

Readers on

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1553 Mendeley
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23 CiteULike
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3 Connotea
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Title
Gene selection and classification of microarray data using random forest
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 1,553 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 8 <1%
Brazil 7 <1%
Australia 7 <1%
India 6 <1%
Spain 6 <1%
Canada 6 <1%
Germany 5 <1%
Italy 4 <1%
Other 31 2%
Unknown 1449 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 396 25%
Researcher 231 15%
Student > Master 218 14%
Student > Bachelor 104 7%
Student > Doctoral Student 78 5%
Other 250 16%
Unknown 276 18%
Readers by discipline Count As %
Computer Science 277 18%
Agricultural and Biological Sciences 257 17%
Engineering 128 8%
Biochemistry, Genetics and Molecular Biology 101 7%
Environmental Science 90 6%
Other 348 22%
Unknown 352 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 09 August 2022.
All research outputs
#3,232,422
of 26,017,215 outputs
Outputs from BMC Bioinformatics
#997
of 7,793 outputs
Outputs of similar age
#10,913
of 176,614 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 41 outputs
Altmetric has tracked 26,017,215 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 7,793 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. 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 176,614 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% 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 particularly well, scoring higher than 92% of its contemporaries.