ArticleNature communications2021
Peak learning of mass spectrometry imaging data using artificial neural networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.
- Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications.Journal of biomedical science · 2026Pooled it
- Recent progress in nanomaterial-enhanced SALDI-MSI for spatial metabolomics.Analytical and bioanalytical chemistry · 2026Review
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Imaging strategies for delineating glioma margins: Aiming at precise therapy.Fundamental research · 2026Review
- Towards precision oncology: unsupervised manifold learning for spatial molecular profiling in cancer tissues.BMC bioinformatics · 2026Article
- A Novel Peak-Shape Aware Approach for Mass Alignment in Mass Spectrometry.Rapid communications in mass spectrometry : RCM · 2026Article
- Machine Learning for Individual EV Classification Based on Highly Sensitive Multiplexed Mass Spectrometry Measurements.Journal of the American Society for Mass Spectrometry · 2026Article
- Signal Strength Aware Latent Spaces Reveal Molecularly Distinct Substructures within Human Kidney Tissue.bioRxiv : the preprint server for biology · 2026Article
- Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.Analytica chimica acta · 2025Review
- De-MSI: A Deep Learning-Based Data Denoising Method to Enhance Mass Spectrometry Imaging by Leveraging the Chemical Prior Knowledge.Analytical chemistry · 2025Article
- A New Approach to Large Multiomics Data Integration.Analytical chemistry · 2025Article
- Deep-learning-enabled online mass spectrometry of the reaction product of a single catalyst nanoparticle.Nature communications · 2025Article
- A Fast Neural Network for Isotopic Charge State Assignment.Journal of the American Chemical Society · 2025Article
- Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review.Mass spectrometry (Tokyo, Japan) · 2025Review
- Multimodal Mass Spectrometry Imaging in Atlas Building: A Review.Seminars in nephrology · 2024Review
- Mass Spectrometry Advancements and Applications for Biomarker Discovery, Diagnostic Innovations, and Personalized Medicine.International journal of molecular sciences · 2024Review
- [Research progress of deep learning applications in mass spectrometry imaging data analysis].Se pu = Chinese journal of chromatography · 2024Review
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors.NPJ systems biology and applications · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
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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.