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A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Overview of attention for article published in BMC Bioinformatics, August 2014
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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2 Facebook pages

Citations

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52 Mendeley
Title
A feature selection method for classification within functional genomics experiments based on the proportional overlapping score
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-274
Pubmed ID
Authors

Osama Mahmoud, Andrew Harrison, Aris Perperoglou, Asma Gul, Zardad Khan, Metodi V Metodiev, Berthold Lausen

Abstract

Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 51 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Student > Master 9 17%
Researcher 8 15%
Student > Doctoral Student 4 8%
Student > Bachelor 3 6%
Other 7 13%
Unknown 9 17%
Readers by discipline Count As %
Computer Science 21 40%
Agricultural and Biological Sciences 7 13%
Medicine and Dentistry 4 8%
Biochemistry, Genetics and Molecular Biology 3 6%
Engineering 3 6%
Other 2 4%
Unknown 12 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 April 2017.
All research outputs
#5,873,235
of 22,759,618 outputs
Outputs from BMC Bioinformatics
#2,165
of 7,273 outputs
Outputs of similar age
#55,037
of 230,877 outputs
Outputs of similar age from BMC Bioinformatics
#43
of 117 outputs
Altmetric has tracked 22,759,618 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 69% 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 230,877 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 75% of its contemporaries.
We're also able to compare this research output to 117 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 63% of its contemporaries.