ArticleBioData mining2017
Label-free data standardization for clinical metabolomics.
Article in BioData mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Accurate Prediction ofMetabolites · 2024Article
- Quick tips for re-using metabolomics data.Nature cell biology · 2022Article
- Metabolomic Laboratory-Developed Tests: Current Status and Perspectives.Metabolites · 2021Review
- Holistic Metabolomic Laboratory-Developed Test (LDT): Development and Use for the Diagnosis of Early-Stage Parkinson's Disease.Metabolites · 2020Article
- Metabolomics Community in Russia: History of Development, Key Participants, and Results.Biotech (Basel (Switzerland)) · 2020Review
- Diagnosis of Parkinson's Disease by A Metabolomics-Based Laboratory-Developed Test (LDT).Diagnostics (Basel, Switzerland) · 2020Article
- Metrics for evaluating the stability and reproducibility of mass spectra.Scientific reports · 2019Article
- Predicting metabolite-disease associations based on KATZ model.BioData mining · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn metabolomics, thousands of substances can be detected in a single assay. This capacity motivates the development of metabolomics testing, which is currently a very promising option for improving laboratory diagnostics. However, the simultaneous measurement of an enormous number of substances leads to metabolomics data often representing concentrations only in conditional units, while laboratory diagnostics generally require actual concentrations. To convert metabolomics data to actual concentrations, calibration curves need to be generated for each substance, and this process represents a significant challenge due to the number of substances that are present in the metabolomics data. To overcome this limitation, a label-free standardization algorithm for metabolomics data is required.
resultsIt was discovered that blood plasma has a set of stable internal standards. The appropriate usage of these newly discovered internal standards provides a background for the label-free standardization of metabolomics data that underlies the SantaOmics (Standardization algorithm for nonlinearly transformed arrays in Omics) algorithm. In this study, using the knee point, it was shown that the metabolomics data can be converted by SantaOmics into a standardized scale that can substitute actual concentration measurements, thus making the metabolomics data directly comparable with each other as well as with reference data presented in the same scale.
conclusionThe developed algorithm sufficiently facilitates the usage of metabolomics data in laboratory diagnostics.
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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.