ReviewMass spectrometry reviews2020
Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry.
Review in Mass spectrometry reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 1 of them a synthesis that pooled it.
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
97 citing papers in PubMed, 1 synthesis or guideline pooled it, 278 citations in OpenAlex.
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.Lipids in health and disease · 2021Trial
- MALDI mass spectrometry imaging across spatial scales in toxicology: tissue evidence, cellular heterogeneity, and emerging single-cell opportunities.Analytical and bioanalytical chemistry · 2026Review
- Insights From Spatiality: Accelerating Pharmaceutical Research Using Mass Spectrometry Imaging.Medicinal research reviews · 2026Review
- Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactiveScience advances · 2026Article
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Machine Learning-Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study.JMIR aging · 2026Article
- Structure-preserving multivariate hypothesis testing for mass spectrometry imaging and single-cell data.Bioinformatics (Oxford, England) · 2026Article
- Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation.Analytical and bioanalytical chemistry · 2026Review
- Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.Analytica chimica acta · 2026Review
- spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass Spectrometry Imaging Data.Analytical chemistry · 2026Article
- Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry.Nature reviews. Methods primers · 2026Article
- Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.Analytica chimica acta · 2025Review
- Topological segmentation of mass spectrometry imaging data.Journal of mass spectrometry and advances in the clinical lab · 2025Article
- Unsupervised spectra information extraction using physics-informed neural networks in the presence of non-linearities and multi-agent problems.Scientific reports · 2025Article
- Prediction of clinical outcomes of ST-elevated myocardial infarction patients using atmospheric solids analysis probe mass spectrometry and machine learning.The Analyst · 2025Article
- Advanced Dimensionality Reduction for Imaging Mass Spectrometry of Human Eye Tissue through Low-Rank Modeling with Sparse and Dense Residuals.Analytical chemistry · 2025Article
- Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis.bioRxiv : the preprint server for biology · 2025Article
- De-MSI: A Deep Learning-Based Data Denoising Method to Enhance Mass Spectrometry Imaging by Leveraging the Chemical Prior Knowledge.Analytical chemistry · 2025Article
- Mass Spectrometry Imaging.Analytical chemistry · 2025Review
37 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.
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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.