↓ Skip to main content


Overview of attention for article published in BMC Bioinformatics, January 2006
Altmetric Badge

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 (88th percentile)

Mentioned by

2 tweeters
7 patents


1465 Dimensions

Readers on

1158 Mendeley
23 CiteULike
3 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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 1,158 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%
Brazil 7 <1%
India 7 <1%
Australia 7 <1%
Spain 6 <1%
Canada 6 <1%
Germany 5 <1%
Italy 4 <1%
Other 33 3%
Unknown 1050 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 339 29%
Researcher 210 18%
Student > Master 175 15%
Student > Bachelor 78 7%
Student > Doctoral Student 58 5%
Other 196 17%
Unknown 102 9%
Readers by discipline Count As %
Computer Science 251 22%
Agricultural and Biological Sciences 240 21%
Engineering 91 8%
Biochemistry, Genetics and Molecular Biology 79 7%
Medicine and Dentistry 65 6%
Other 269 23%
Unknown 163 14%

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 29 August 2019.
All research outputs
of 14,876,982 outputs
Outputs from BMC Bioinformatics
of 5,512 outputs
Outputs of similar age
of 91,060 outputs
Outputs of similar age from BMC Bioinformatics
of 1 outputs
Altmetric has tracked 14,876,982 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,512 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 88% 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 91,060 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 88% 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