ArticleBioinformatics (Oxford, England)2020
ColocML: machine learning quantifies co-localization between mass spectrometry images.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 51 citations in OpenAlex.
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
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- Computational method for mapping mass signatures along developmental gradients reveals a novel role for a monosaccharide tetrose in maize salt-stress response.bioRxiv : the preprint server for biology · 2025Article
- Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique.Scientific reports · 2025Article
- ICOBA: A highly customizable iterative imagej macro for optimization of image co-localization batch analysis.SoftwareX · 2025Article
- Optimizing Colocalized Cell Counting Using Automated and Semiautomated Methods.Neuroinformatics · 2025Article
- Utilization of a natural language processing-based approach to determine the composition of artifact residues.BMC bioinformatics · 2024Article
- [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
- DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.Analytical chemistry · 2024Article
- Review
- High-Throughput Deconvolution of Native Protein Mass Spectrometry Imaging Data Sets for Mass Domain Analysis.Analytical chemistry · 2023Article
- rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation.Journal of cheminformatics · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives.Frontiers in chemistry · 2022Review
- When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective.Frontiers in oncology · 2022Review
- Self-supervised clustering of mass spectrometry imaging data using contrastive learning.Chemical science · 2021Article
- Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning.Analytical and bioanalytical chemistry · 2021Article
- Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding.Analytical chemistry · 2021Article
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationImaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development.
resultsWe present ColocML, a machine learning approach addressing this gap. With the help of 42 imaging MS experts from nine laboratories, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using term frequency-inverse document frequency and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings, respectively). We illustrate these measures by inferring co-localization properties of 10 273 molecules from 3685 public METASPACE datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/metaspace2020/coloc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.