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January 1, 2025The AnalystOpen Access

Unsupervised Machine Learning for Mass Spectrometry Imaging Data Analysis with in vivo Isotope Labeling

Raven L. Buckman Johnson Vy T. Tat Young‐Jin Lee

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Key Points

Unsupervised image segmentation enhances analysis of mass spectrometry imaging data, improving interpretation accuracy.
The study reports successful in vivo isotope labeling in duckweed for MALDI-MSI analysis, facilitating better data extraction.
Analysis utilized unsupervised machine learning techniques to process complex imaging data effectively.
These findings highlight the potential for advanced data analysis methods in scientific imaging applications.

Implication

This research shows unsupervised segmentation of MALDI-MSI data in duckweed, suggesting new analytical approaches.

Abstract

In this study, in vivo isotope labeling of duckweed for MALDI-MSI analysis is performed with subsequent unsupervised image segmentation.

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Cite This Study

Johnson et al. (2025) studied this question.

synapsesocial.com/papers/68c1d7e354b1d3bfb60f9bac https://doi.org/10.1039/d5an00649j

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