ArticleJournal of proteome research2013
Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data.
Article in Journal of proteome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed, 84 citations in OpenAlex.
- Particulate metal exposures induce plasma metabolome changes in a commuter panel study.PloS one · 2018Trial
- Novel metabolic markers for the risk of diabetes development in American Indians.Diabetes care · 2015Trial
- Sex-Associated Effects of Thioredoxin Reductase Inhibition in Hyperoxia-Induced Lung Injury in Adult Mice.Metabolites · 2026Article
- Serum metabolomics in early pregnancy differentiates between gestational hypertension and preeclampsia.BMC pregnancy and childbirth · 2026Article
- Altered Lipid and Neurotransmitter Metabolism as Potential Mechanisms of Weight Gain in Women With HIV Initiating INSTIs in a Pilot Study.Journal of acquired immune deficiency syndromes (1999) · 2026Article
- Metabolomics Data Processing Using the Asari Toolkit.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Metabolomic Associations With Fatigue and Physical Function in Children With Cancer: A Pilot Study.Biological research for nursing · 2025Article
- Global metabolomic alterations associated with endocrine-disrupting chemicals among pregnant individuals and newborns.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Plasma metabolomics profiles and breast cancer risk.Breast cancer research : BCR · 2024Article
- Metabolomic Effects of Folic Acid Supplementation in Adults: Evidence from the FACT Trial.The Journal of nutrition · 2024Article
- Dioxin(-like)-Related Biological Effects through Integrated Chemical-wide and Metabolome-wide Analyses.Environmental science & technology · 2024Article
- Maternal and newborn metabolomic changes associated with urinary polycyclic aromatic hydrocarbon metabolite concentrations at delivery: an untargeted approach.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- Trackable and scalable LC-MS metabolomics data processing using asari.Nature communications · 2023Article
- Article
- Metabolomic changes associated with chronic arsenic exposure in a Bangladeshi population.Chemosphere · 2023Article
- Study on the relationship between selenium and cadmium in diseased human lungs.Advances in redox research · 2023Article
- Metabolome × Microbiome Changes Associated with a Diet-Induced Reduction in Hepatic Fat among Adolescent Boys.Metabolites · 2023Article
- Metabolomics profiling in acute liver transplant rejection in a pediatric population.Scientific reports · 2022Article
- Transcriptomic-Metabolomic Profiling in Mouse Lung Tissues Reveals Sex- and Strain-Based Differences.Metabolites · 2022Article
- Metabolic Pathways Associated With Psychoneurological Symptoms in Children With Cancer Receiving Chemotherapy.Biological research for nursing · 2022Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/ .
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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.