ArticleBriefings in bioinformatics2024
Ion entropy and accurate entropy-based FDR estimation in metabolomics.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed.
- ExpoLib: a framework for an MS/MS exposome library of anthropogenic and natural toxicants and their biotransformation products.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- ProbioticsFoods (Basel, Switzerland) · 2026Article
- Mass Spectrometry-Based Metabolomics in Pediatric Health and Disease.Metabolites · 2026Review
- Data Processing of Product Ion Spectra: Methods to Control False Discovery Rate in Compound Search Results for Untargeted Metabolomics.Mass spectrometry (Tokyo, Japan) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.
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