ArticleMetabolomics : Official journal of the Metabolomic Society2018
Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles.
Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Alternative weighting schemes for fine-tuned extended similarity indices.Journal of chemometrics · 2024Article
- Extended continuous similarity indices: theory and application for QSAR descriptor selection.Journal of computer-aided molecular design · 2022Article
- Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development.Foods (Basel, Switzerland) · 2021Article
- Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: Theory and characteristicsJournal of cheminformatics · 2021Article
- Extended many-item similarity indices for sets of nucleotide and protein sequences.Computational and structural biotechnology journal · 2021Article
- Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR.Molecular informatics · 2019Article
- Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.Molecules (Basel, Switzerland) · 2019Article
- Similarity and differential NMR spectroscopy in metabolomics: application to the analysis of vegetable oils withMetabolomics : Official journal of the Metabolomic Society · 2019Article
- Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints.Journal of cheminformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionContemporary 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.
objectivesThe 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.
methodsNine 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.
resultsBaroni-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.
conclusionFingerprint 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.