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Supervised redundant feature detection for tumor classification.

Overview of attention for article published in BMC Medical Genomics, October 2014
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Mentioned by

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1 tweeter

Citations

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4 Dimensions

Readers on

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5 Mendeley
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Title
Supervised redundant feature detection for tumor classification.
Published in
BMC Medical Genomics, October 2014
DOI 10.1186/1755-8794-7-s2-s5
Pubmed ID
Authors

Xue-Qiang Zeng, Guo-Zheng Li

Abstract

As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features affect overall performance of classifiers.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Professor > Associate Professor 1 20%
Lecturer 1 20%
Student > Doctoral Student 1 20%
Readers by discipline Count As %
Computer Science 4 80%
Agricultural and Biological Sciences 1 20%

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 01 July 2015.
All research outputs
#2,815,224
of 5,302,711 outputs
Outputs from BMC Medical Genomics
#219
of 351 outputs
Outputs of similar age
#77,199
of 150,042 outputs
Outputs of similar age from BMC Medical Genomics
#11
of 15 outputs
Altmetric has tracked 5,302,711 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 351 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 23rd percentile – i.e., 23% 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 150,042 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.