ArticleMetabolites2022
Comprehensive Peak Characterization (CPC) in Untargeted LC-MS Analysis.
Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed.
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Retrospective Urine Metabolomics of Clinical Toxicology Samples Reveals Features Associated with Cocaine Exposure.Metabolites · 2025Article
- Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites.Animals : an open access journal from MDPI · 2024Review
- Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics.BMC bioinformatics · 2023Article
- Comparative metabolomics of root-tips reveals distinct metabolic pathways conferring drought tolerance in contrasting genotypes of rice.BMC genomics · 2023Article
- AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications.Cell reports. Physical science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
LC-MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.
Indexed as
Identifiers
What OpenQuestion holds
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.