ArticleIEEE/ACM transactions on computational biology and bioinformatics
Strategies for Comparing Metabolic Profiles: Implications for the Inference of Biochemical Mechanisms from Metabolomics Data.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Complex system modeling reveals oxalate homeostasis is driven by diverse oxalate-degrading bacteria.eLife · 2025Article
- Phenotypic and metabolomic characteristics of mouse models of metabolic associated steatohepatitis.Biomarker research · 2024Article
- Metabolic Modeling Identifies a Novel Molecular Type of Glioblastoma Associated with Good Prognosis.Metabolites · 2023Article
- Targeted metabolomics characterizes metabolite occurrence and variability in stable freshwater mussel populations.Conservation physiology · 2023Article
- Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.BMC bioinformatics · 2022Article
- Delineating the Role of the Urinary Metabolome in the Lithogenesis of Calcium-Based Kidney Stones.Urology · 2022Article
- A postbiotic consisting of heat-treated lactobacilli has a bifidogenic effect in pure culture and in human fermented faecal communities.Applied and environmental microbiology · 2021Article
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2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundLarge amounts of metabolomics data have been accumulated in recent years and await analysis. Previously, we had developed a systems biology approach to infer biochemical mechanisms underlying metabolic alterations observed in cancers and other diseases. The method utilized the typical Euclidean distance for comparing metabolic profiles. Here, we ask whether any of the numerous alternative metrics might serve this purpose better. METHODS AND
findingsWe used enzymatic alterations in purine metabolism that were measured in human renal cell carcinoma to test various metrics with the goal of identifying the best metrics for discerning metabolic profiles of healthy and diseased individuals. The results showed that several metrics have similarly good performance, but that some are unsuited for comparisons of metabolic profiles. Furthermore, the results suggest that relative changes in metabolite levels, which reduce bias toward large metabolite concentrations, are better suited for comparisons of metabolic profiles than absolute changes. Finally, we demonstrate that a sequential search for enzymatic alterations, ranked by importance, is not always valid.
conclusionsWe identified metrics that are appropriate for comparisons of metabolic profiles. In addition, we constructed strategic guidelines for the algorithmic identification of biochemical mechanisms from metabolomics data.
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