Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia.

Journal: Computers in biology and medicine
Published Date: Jun 13, 2024

Abstract

BACKGROUND: Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction.

Authors

  • Α Kosvyra
    Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: aekosvyra@auth.gr.
  • Α Karadimitris
    Centre for Haematology and Hugh and Josseline Langmuir Centre for Myeloma Research, Department of Immunology and Inflammation, Imperial College London, Department of Haematology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, Du Cane Road, London, W12 0NN, UK.
  • Μ Papaioannou
    Hematology Unit, 1st Dept of Internal Medicine, AHEPA Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
  • I Chouvarda
    Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.

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