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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)

Mentioned by

1 news outlet
2 tweeters
7 patents


1778 Dimensions

Readers on

1327 Mendeley
23 CiteULike
3 Connotea
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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

Ramón Díaz-Uriarte, Sara Alvarez de Andrés


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 1,327 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%
Australia 7 <1%
Brazil 7 <1%
India 7 <1%
Spain 6 <1%
Canada 6 <1%
Germany 5 <1%
Italy 4 <1%
Other 32 2%
Unknown 1221 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 373 28%
Researcher 221 17%
Student > Master 202 15%
Student > Bachelor 91 7%
Student > Doctoral Student 67 5%
Other 214 16%
Unknown 159 12%
Readers by discipline Count As %
Computer Science 258 19%
Agricultural and Biological Sciences 244 18%
Engineering 110 8%
Biochemistry, Genetics and Molecular Biology 93 7%
Environmental Science 78 6%
Other 307 23%
Unknown 237 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 15 September 2020.
All research outputs
of 18,890,258 outputs
Outputs from BMC Bioinformatics
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Outputs of similar age
of 101,240 outputs
Outputs of similar age from BMC Bioinformatics
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Altmetric has tracked 18,890,258 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,459 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 95% 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 101,240 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them