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Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 13% |
| Unknown | 7 | 88% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 50% |
| Scientists | 2 | 25% |
| Science communicators (journalists, bloggers, editors) | 2 | 25% |
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 % |
|---|---|---|
| Unknown | 40 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 20% |
| Researcher | 7 | 18% |
| Student > Master | 6 | 15% |
| Other | 2 | 5% |
| Professor > Associate Professor | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 11 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 20% |
| Biochemistry, Genetics and Molecular Biology | 5 | 13% |
| Chemistry | 5 | 13% |
| Engineering | 2 | 5% |
| Environmental Science | 1 | 3% |
| Other | 4 | 10% |
| Unknown | 15 | 38% |
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
#7,034,523
of 23,023,224 outputs
Outputs from Metabolomics
#424
of 1,300 outputs
Outputs of similar age
#144,299
of 440,208 outputs
Outputs of similar age from Metabolomics
#19
of 36 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,300 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 65% 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 440,208 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 36 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.