IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-52213-9.html
   My bibliography  Save this article

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

Author

Listed:
  • Bishoy Wadie

    (European Molecular Biology Laboratory (EMBL)
    Faculty of Biosciences)

  • Lachlan Stuart

    (European Molecular Biology Laboratory (EMBL))

  • Christopher M. Rath

    (European Molecular Biology Laboratory (EMBL))

  • Bernhard Drotleff

    (EMBL)

  • Sergii Mamedov

    (European Molecular Biology Laboratory (EMBL))

  • Theodore Alexandrov

    (European Molecular Biology Laboratory (EMBL)
    EMBL
    EMBL
    BioInnovation Institute)

Abstract

Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

Suggested Citation

  • Bishoy Wadie & Lachlan Stuart & Christopher M. Rath & Bernhard Drotleff & Sergii Mamedov & Theodore Alexandrov, 2024. "METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52213-9
    DOI: 10.1038/s41467-024-52213-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-52213-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-52213-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52213-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.