↓ Skip to main content

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Overview of attention for article published in BMC Bioinformatics, July 2008
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 (87th percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters

Citations

dimensions_citation
461 Dimensions

Readers on

mendeley
515 Mendeley
citeulike
16 CiteULike
connotea
2 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.
Title
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Published in
BMC Bioinformatics, July 2008
DOI 10.1186/1471-2105-9-319
Pubmed ID
Authors

Alexander Statnikov, Lily Wang, Constantin F Aliferis

Abstract

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 515 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 2%
Canada 6 1%
France 5 <1%
Sweden 4 <1%
Germany 4 <1%
Spain 3 <1%
Netherlands 2 <1%
India 2 <1%
Malaysia 2 <1%
Other 9 2%
Unknown 467 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 139 27%
Researcher 80 16%
Student > Master 73 14%
Student > Bachelor 41 8%
Student > Doctoral Student 24 5%
Other 85 17%
Unknown 73 14%
Readers by discipline Count As %
Computer Science 113 22%
Agricultural and Biological Sciences 87 17%
Engineering 52 10%
Biochemistry, Genetics and Molecular Biology 43 8%
Medicine and Dentistry 26 5%
Other 98 19%
Unknown 96 19%

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 19 July 2013.
All research outputs
#2,278,238
of 17,351,915 outputs
Outputs from BMC Bioinformatics
#895
of 6,150 outputs
Outputs of similar age
#16,252
of 131,896 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 17,351,915 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,150 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 85% 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 131,896 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 87% 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