A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers.

Journal: BMC bioinformatics
PMID: 39934670

Abstract

BACKGROUND: Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts.

Authors

  • Luke Kennedy
    Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada.
  • Jagdeep K Sandhu
    Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada.
  • Mary-Ellen Harper
    Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada. mharper@uottawa.ca.
  • Miroslava Cuperlovic-Culf
    Digital Technologies Research Center, National Research Council Canada, Ottawa, ON, Canada.

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