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Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution

Overview of attention for article published in BMC Bioinformatics, May 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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4 X users

Citations

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3 Dimensions

Readers on

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40 Mendeley
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1 CiteULike
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Title
Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution
Published in
BMC Bioinformatics, May 2013
DOI 10.1186/1471-2105-14-155
Pubmed ID
Authors

Marie C Galligan, Radka Saldova, Matthew P Campbell, Pauline M Rudd, Thomas B Murphy

Abstract

Glycoproteins are involved in a diverse range of biochemical and biological processes. Changes in protein glycosylation are believed to occur in many diseases, particularly during cancer initiation and progression. The identification of biomarkers for human disease states is becoming increasingly important, as early detection is key to improving survival and recovery rates. To this end, the serum glycome has been proposed as a potential source of biomarkers for different types of cancers.High-throughput hydrophilic interaction liquid chromatography (HILIC) technology for glycan analysis allows for the detailed quantification of the glycan content in human serum. However, the experimental data from this analysis is compositional by nature. Compositional data are subject to a constant-sum constraint, which restricts the sample space to a simplex. Statistical analysis of glycan chromatography datasets should account for their unusual mathematical properties.As the volume of glycan HILIC data being produced increases, there is a considerable need for a framework to support appropriate statistical analysis. Proposed here is a methodology for feature selection in compositional data. The principal objective is to provide a template for the analysis of glycan chromatography data that may be used to identify potential glycan biomarkers.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 3%
Denmark 1 3%
Ireland 1 3%
Unknown 37 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 25%
Student > Ph. D. Student 6 15%
Student > Master 5 13%
Student > Doctoral Student 4 10%
Professor > Associate Professor 4 10%
Other 5 13%
Unknown 6 15%
Readers by discipline Count As %
Computer Science 7 18%
Agricultural and Biological Sciences 6 15%
Mathematics 4 10%
Biochemistry, Genetics and Molecular Biology 3 8%
Medicine and Dentistry 3 8%
Other 9 23%
Unknown 8 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 September 2016.
All research outputs
#7,328,935
of 22,709,015 outputs
Outputs from BMC Bioinformatics
#2,978
of 7,256 outputs
Outputs of similar age
#64,463
of 193,543 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 124 outputs
Altmetric has tracked 22,709,015 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,256 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 58% 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 193,543 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 124 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 52% of its contemporaries.