ArticlePatterns (New York, N.Y.)2021
Machine-learning-enhanced time-of-flight mass spectrometry analysis.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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.
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Who cites it
5 citing papers in PubMed.
- Automatic recognition of Pd ions in high-resolution mass spectra of multicomponent samples without visible fine isotope structures.Nature communications · 2026Article
- Automated Peak Annotation in Time-of-Flight Secondary Ion Mass Spectrometry via a Physics-Informed Probabilistic Framework.Molecules (Basel, Switzerland) · 2026Article
- Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data.Nature communications · 2025Article
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.
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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.