↓ Skip to main content

MetaFIND: A feature analysis tool for metabolomics data

Overview of attention for article published in BMC Bioinformatics, November 2008
Altmetric Badge

Mentioned by

facebook
1 Facebook page

Citations

dimensions_citation
31 Dimensions

Readers on

mendeley
97 Mendeley
citeulike
3 CiteULike
connotea
1 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
MetaFIND: A feature analysis tool for metabolomics data
Published in
BMC Bioinformatics, November 2008
DOI 10.1186/1471-2105-9-470
Pubmed ID
Authors

Kenneth Bryan, Lorraine Brennan, Pádraig Cunningham

Abstract

Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
Spain 2 2%
Ireland 1 1%
Austria 1 1%
Netherlands 1 1%
Canada 1 1%
Portugal 1 1%
Argentina 1 1%
United States 1 1%
Other 0 0%
Unknown 86 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 35%
Student > Ph. D. Student 22 23%
Student > Bachelor 7 7%
Professor 6 6%
Professor > Associate Professor 5 5%
Other 16 16%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 40%
Chemistry 12 12%
Biochemistry, Genetics and Molecular Biology 7 7%
Computer Science 6 6%
Engineering 6 6%
Other 18 19%
Unknown 9 9%
Attention Score in Context

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 03 July 2014.
All research outputs
#20,232,430
of 22,758,248 outputs
Outputs from BMC Bioinformatics
#6,844
of 7,272 outputs
Outputs of similar age
#89,244
of 92,914 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 48 outputs
Altmetric has tracked 22,758,248 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 1st percentile – i.e., 1% 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 92,914 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.