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Feature selection of gene expression data for Cancer classification using double RBF-kernels

Overview of attention for article published in BMC Bioinformatics, October 2018
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Mentioned by

twitter
2 tweeters

Citations

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19 Dimensions

Readers on

mendeley
34 Mendeley
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Title
Feature selection of gene expression data for Cancer classification using double RBF-kernels
Published in
BMC Bioinformatics, October 2018
DOI 10.1186/s12859-018-2400-2
Authors

Shenghui Liu, Chunrui Xu, Yusen Zhang, Jiaguo Liu, Bin Yu, Xiaoping Liu, Matthias Dehmer

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

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 15%
Student > Master 5 15%
Researcher 4 12%
Student > Bachelor 4 12%
Student > Postgraduate 3 9%
Other 3 9%
Unknown 10 29%
Readers by discipline Count As %
Computer Science 12 35%
Biochemistry, Genetics and Molecular Biology 4 12%
Engineering 4 12%
Economics, Econometrics and Finance 1 3%
Mathematics 1 3%
Other 3 9%
Unknown 9 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 30 October 2018.
All research outputs
#10,457,359
of 13,715,693 outputs
Outputs from BMC Bioinformatics
#3,950
of 5,103 outputs
Outputs of similar age
#217,747
of 309,337 outputs
Outputs of similar age from BMC Bioinformatics
#295
of 412 outputs
Altmetric has tracked 13,715,693 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,103 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% 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 309,337 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 412 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.