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New bandwidth selection criterion for Kernel PCA: Approach to dimensionality reduction and classification problems

Overview of attention for article published in BMC Bioinformatics, May 2014
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
61 Mendeley
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Title
New bandwidth selection criterion for Kernel PCA: Approach to dimensionality reduction and classification problems
Published in
BMC Bioinformatics, May 2014
DOI 10.1186/1471-2105-15-137
Pubmed ID
Authors

Minta Thomas, Kris De Brabanter, Bart De Moor

Abstract

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques.

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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Netherlands 1 2%
India 1 2%
Brazil 1 2%
Unknown 56 92%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 23%
Student > Ph. D. Student 12 20%
Researcher 11 18%
Student > Doctoral Student 7 11%
Student > Bachelor 4 7%
Other 8 13%
Unknown 5 8%
Readers by discipline Count As %
Computer Science 13 21%
Agricultural and Biological Sciences 11 18%
Medicine and Dentistry 9 15%
Engineering 7 11%
Biochemistry, Genetics and Molecular Biology 6 10%
Other 10 16%
Unknown 5 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 May 2015.
All research outputs
#13,325,956
of 21,353,399 outputs
Outputs from BMC Bioinformatics
#4,492
of 6,933 outputs
Outputs of similar age
#105,672
of 204,737 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 18 outputs
Altmetric has tracked 21,353,399 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,933 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 204,737 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.