ArticleChemical science2021
Self-supervised clustering of mass spectrometry imaging data using contrastive learning.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- Research Progress on the Application of Mass Spectrometry Imaging Technology in Cerebral Disease.Life (Basel, Switzerland) · 2026Review
- Efficient Compression of Mass Spectrometry Images via Contrastive Learning-Based Encoding.Analytical chemistry · 2025Article
- Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.Nature communications · 2025Article
- Towards robust medical machine olfaction: Debiasing GC-MS data enhances prostate cancer diagnosis from urine volatiles.PloS one · 2025Article
- [Research progress of deep learning applications in mass spectrometry imaging data analysis].Se pu = Chinese journal of chromatography · 2024Review
- DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.Analytical chemistry · 2024Article
- pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.Bioinformatics (Oxford, England) · 2024Article
- Multimodal high-resolution nano-DESI MSI and immunofluorescence imaging reveal molecular signatures of skeletal muscle fiber types.Chemical science · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning.Journal of cheminformatics · 2022Article
- High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling.ACS measurement science au · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. One of key challenges in molecular colocalization is that complex MSI data are too large for manual annotation but too small for training deep neural networks. Herein, we introduce a self-supervised clustering approach based on contrastive learning, which shows an excellent performance in clustering of MSI data. We train a deep convolutional neural network (CNN) using MSI data from a single experiment without manual annotations to effectively learn high-level spatial features from ion images and classify them based on molecular colocalizations. We demonstrate that contrastive learning generates ion image representations that form well-resolved clusters. Subsequent self-labeling is used to fine-tune both the CNN encoder and linear classifier based on confidently classified ion images. This new approach enables autonomous and high-throughput identification of co-localized species in MSI data, which will dramatically expand the application of spatial lipidomics, metabolomics, and proteomics in biological research.
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Registered trials
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