Exploring machine learning for untargeted metabolomics using molecular fingerprints.

Journal: Computer methods and programs in biomedicine
PMID: 38626559

Abstract

BACKGROUND: Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways.

Authors

  • Christel Sirocchi
    Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy. Electronic address: c.sirocchi2@campus.uniurb.it.
  • Federica Biancucci
    Department of Biomolecular Sciences, University of Urbino, Via Saffi 2, Urbino, 61029, Italy.
  • Matteo Donati
    Department of Pure and Applied Sciences, University of Urbino Piazza della Repubblica 13, 61029 Urbino, Italy.
  • Alessandro Bogliolo
    Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy.
  • Mauro Magnani
    Department of Biomolecular Sciences, University of Urbino, Via Saffi 2, Urbino, 61029, Italy.
  • Michele Menotta
    Department of Biomolecular Sciences, University of Urbino, Via Saffi 2, Urbino, 61029, Italy.
  • Sara Montagna
    DISI-University of Bologna, Cesena, Italy.

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