ArticleBioinformatics (Oxford, England)2014
Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Fecal microbiota transplantation promotes reduction of antimicrobial resistance by strain replacement.Science translational medicine · 2023Trial
- Particulate metal exposures induce plasma metabolome changes in a commuter panel study.PloS one · 2018Trial
- Review
- Early-Life Air Pollution Exposure Is Associated with the Infant Gut Microbiome and Fecal Metabolome in the First Two Years of Life.Research report (Health Effects Institute) · 2026Article
- Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows.Analytical chemistry · 2025Article
- Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical Comparisons between Samples.Journal of proteome research · 2025Article
- Prenatal Nitrogen Oxide (NOEnvironmental science & technology · 2025Article
- TIHI Toolkit: A Peak Finder and Analyzer for Spectroscopic Data.ACS omega · 2024Article
- Prenatal and Early Life Exposure to Ambient Air Pollutants Is Associated with the Fecal Metabolome in the First Two Years of Life.Environmental science & technology · 2024Article
- Human Milk Composition Is Associated with Maternal Body Mass Index in a Cross-Sectional, Untargeted Metabolomics Analysis of Human Milk from Guatemalan Mothers.Current developments in nutrition · 2024Article
- Metabolomic Effects of Folic Acid Supplementation in Adults: Evidence from the FACT Trial.The Journal of nutrition · 2024Article
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
- Metabolomic changes associated with chronic arsenic exposure in a Bangladeshi population.Chemosphere · 2023Article
- Longitudinal profiles of the fecal metabolome during the first 2 years of life.Scientific reports · 2023Article
- The potential role of early life feeding patterns in shaping the infant fecal metabolome: implications for neurodevelopmental outcomes.npj metabolic health and disease · 2023Article
- Use of high-resolution metabolomics to assess the biological perturbations associated with maternal exposure to Bisphenol A and Bisphenol F among pregnant African American women.Environment international · 2022Article
- Operationalizing the Exposome Using Passive Silicone Samplers.Current pollution reports · 2022Review
- Per- and polyfluoroalkyl substance (PFAS) exposure, maternal metabolomic perturbation, and fetal growth in African American women: A meet-in-the-middle approach.Environment international · 2022Article
- Microbial metabolite delta-valerobetaine is a diet-dependent obesogen.Nature metabolism · 2021Article
- Plasma Metabolic Phenotypes of HPV-Associated versus Smoking-Associated Head and Neck Cancer and Patient Survival.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
Corrections and comments
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2 authors.
Funding
Abstract
motivationPeak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics.
resultsHere we present a method that learns directly from various data features of the extracted ion chromatograms (EICs) to differentiate between true peak regions from noise regions in the LC/MS profile. It utilizes the knowledge of known metabolites, as well as robust machine learning approaches. Unlike currently available methods, this new approach does not assume a parametric peak shape model and allows maximum flexibility. We demonstrate the superiority of the new approach using real data. Because matching to known metabolites entails uncertainties and cannot be considered a gold standard, we also developed a probabilistic receiver-operating characteristic (pROC) approach that can incorporate uncertainties. AVAILABILITY AND IMPLEMENTATION: The new peak detection approach is implemented as part of the apLCMS package available at http://web1.sph.emory.edu/apLCMS/ CONTACT: tyu8@emory.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.