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Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Overview of attention for article published in Metabolomics, January 2018
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  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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8 tweeters

Citations

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16 Dimensions

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31 Mendeley
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Title
Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
Published in
Metabolomics, January 2018
DOI 10.1007/s11306-018-1327-y
Pubmed ID
Authors

Anita Rácz, Filip Andrić, Dávid Bajusz, Károly Héberger

Abstract

Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Student > Master 6 19%
Researcher 5 16%
Other 2 6%
Professor > Associate Professor 2 6%
Other 3 10%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 23%
Chemistry 5 16%
Biochemistry, Genetics and Molecular Biology 2 6%
Engineering 2 6%
Computer Science 1 3%
Other 4 13%
Unknown 10 32%

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 14 June 2018.
All research outputs
#3,733,589
of 13,087,494 outputs
Outputs from Metabolomics
#289
of 881 outputs
Outputs of similar age
#119,742
of 349,616 outputs
Outputs of similar age from Metabolomics
#12
of 31 outputs
Altmetric has tracked 13,087,494 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 881 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 66% 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 349,616 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 65% of its contemporaries.
We're also able to compare this research output to 31 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 61% of its contemporaries.