ArticleBMC bioinformatics2020
OffsampleAI: artificial intelligence approach to recognize off-sample mass spectrometry images.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 47 citations in OpenAlex.
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- Met-ID: An Open-Source Software for Comprehensive Annotation of Multiple On-Tissue Chemical Modifications in MALDI-MSI.Analytical chemistry · 2025Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Quantitative mass spectrometry imaging (qMSI): A tutorial.Journal of mass spectrometry : JMS · 2024Article
- Development of an object-based image analysis tool for mass spectrometry imaging ion classification.Analytical and bioanalytical chemistry · 2023Article
- Engineering the MoSAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.Bioinformatics (Oxford, England) · 2023Article
- MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective.Current issues in molecular biology · 2023Review
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- The emergence of imaging mass spectrometry in drug discovery and development: Making a difference by driving decision making.Journal of mass spectrometry : JMS · 2021Article
- Facilitating Imaging Mass Spectrometry of Microbial Specialized Metabolites with METASPACE.Metabolites · 2021Article
- Mass Spectrometry Imaging of Fibroblasts: Promise and Challenge.Expert review of proteomics · 2021Review
- Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics.Journal of the American Society for Mass Spectrometry · 2021Review
- Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ).PeerJ. Computer science · 2021Article
- Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence.Annual review of biomedical data science · 2020Article
- ColocML: machine learning quantifies co-localization between mass spectrometry images.Bioinformatics (Oxford, England) · 2020Article
- Methods for Cryosectioning and Mass Spectrometry Imaging of Whole-Body Zebrafish.Journal of the American Society for Mass Spectrometry · 2020Article
- Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundImaging mass spectrometry (imaging MS) is an enabling technology for spatial metabolomics of tissue sections with rapidly growing areas of applications in biology and medicine. However, imaging MS data is polluted with off-sample ions caused by sample preparation, particularly by the MALDI (matrix-assisted laser desorption/ionization) matrix application. Off-sample ion images confound and hinder statistical analysis, metabolite identification and downstream analysis with no automated solutions available.
resultsWe developed an artificial intelligence approach to recognize off-sample ion images. First, we created a high-quality gold standard of 23,238 expert-tagged ion images from 87 public datasets from the METASPACE knowledge base. Next, we developed several machine and deep learning methods for recognizing off-sample ion images. The following methods were able to reproduce expert judgements with a high agreement: residual deep learning (F1-score 0.97), semi-automated spatio-molecular biclustering (F1-score 0.96), and molecular co-localization (F1-score 0.90). In a test-case study, we investigated off-sample images corresponding to the most common MALDI matrix (2,5-dihydroxybenzoic acid, DHB) and characterized properties of matrix clusters.
conclusionsOverall, our work illustrates how artificial intelligence approaches enabled by open-access data, web technologies, and machine and deep learning open novel avenues to address long-standing challenges in imaging MS.
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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.