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Gene Selection for Cancer Classification using Support Vector Machines

Overview of attention for article published in Machine Learning, January 2002
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#12 of 1,259)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
policy
2 policy sources
twitter
5 X users
patent
10 patents
wikipedia
5 Wikipedia pages
q&a
1 Q&A thread

Citations

dimensions_citation
7183 Dimensions

Readers on

mendeley
2796 Mendeley
citeulike
17 CiteULike
Title
Gene Selection for Cancer Classification using Support Vector Machines
Published in
Machine Learning, January 2002
DOI 10.1023/a:1012487302797
Authors

Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 2,796 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 50 2%
United Kingdom 13 <1%
Germany 11 <1%
Malaysia 7 <1%
Italy 7 <1%
France 6 <1%
Canada 6 <1%
Switzerland 5 <1%
Spain 5 <1%
Other 68 2%
Unknown 2618 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 671 24%
Student > Master 424 15%
Researcher 386 14%
Student > Bachelor 172 6%
Student > Doctoral Student 131 5%
Other 425 15%
Unknown 587 21%
Readers by discipline Count As %
Computer Science 754 27%
Engineering 352 13%
Agricultural and Biological Sciences 246 9%
Biochemistry, Genetics and Molecular Biology 137 5%
Medicine and Dentistry 95 3%
Other 480 17%
Unknown 732 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 47. 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 16 November 2023.
All research outputs
#911,083
of 25,837,817 outputs
Outputs from Machine Learning
#12
of 1,259 outputs
Outputs of similar age
#1,082
of 133,610 outputs
Outputs of similar age from Machine Learning
#1
of 8 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,259 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 99% 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 133,610 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 98% of its contemporaries.
We're also able to compare this research output to 8 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