ReviewACS measurement science au2024
Recent Developments in Machine Learning for Mass Spectrometry.
Review in ACS measurement science au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed.
- From Precision Machining to Hybrid Microfabrication: A Review on Quadrupole and Ion Trap Mass Analyzer Manufacturing.Rapid communications in mass spectrometry : RCM · 2026Review
- Machine Learning-Enhanced LC-HRMS Workflows for Suspect and Nontargeted Screening of Pesticide Residues in Fruits and Vegetables.Analytical chemistry · 2026Article
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Integrating Low-Voltage Microfluidics With On-Chip Electrospray Ionization for Miniaturized Protein Analysis.Small methods · 2026Review
- Towards spatial lipid profiling by using mass spectrometry: analytical challenges and applications.Analytical and bioanalytical chemistry · 2026Review
- Enhanced Environmental PFAS Characterization Using a Virtual High-Resolution Mass Spectral Library Generated by Transfer Learning-Based Neural Network.Environmental science & technology · 2026Article
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Wooden-Tip Electrospray Ionization Mass Spectrometry Combined With Machine Learning for Differentiating Thyroid Tumours.Analytical science advances · 2026Article
- AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.Current issues in molecular biology · 2026Review
- Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra.ACS omega · 2026Article
- Prediction of KPC-producingJournal of clinical microbiology · 2026Article
- Ambient high-spatial-resolution mass spectrometry imaging: recent advances in technologies and metabolomics studies.Trends in analytical chemistry : TRAC · 2026Article
- A Lightweight Data-Augmented Deep Learning Framework for Real-Time Instance Segmentation in Liquid-Phase In Situ Transmission Electron Microscopy.ACS measurement science au · 2026Article
- Lightweight liquid neural networks decipher salivary metabolic fingerprinting for high-risk periodontitis screening in diabetes.NPJ digital medicine · 2026Article
- Graph Machine Learning Can Estimate Drug Concentrations in Whole Blood from Forensic Screening Results.Analytical chemistry · 2026Article
- Rapid and noninvasive artificial intelligence-assisted diagnostic method for oral squamous cell carcinoma.NPJ digital medicine · 2026Article
- Applications and technical challenges of MALDI mass spectrometry imaging in microbial ecosystems and metabolic network research.Archives of microbiology · 2026Review
- De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations.Molecules (Basel, Switzerland) · 2026Review
- Molecular Biointerface Characterization for an Implanted Medical Device Using Cryogenic Orbitrap Secondary Ion Mass Spectrometry (Cryo-OrbiSIMS).ACS applied materials & interfaces · 2026Article
- Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.Analytica chimica acta · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.
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.