Comparative analysis of statistical and deep learning-based multi-omics integration for breast cancer subtype classification.

Journal: Journal of translational medicine
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Breast cancer (BC) is a critical cause of cancer-related death globally. The heterogeneity of BC subtypes poses challenges in understanding molecular mechanisms, early diagnosis, and disease management. Recent studies suggest that integrating multi-omics layers can significantly enhance BC subtype identification. However, evaluating different multi-omics integration methods for BC subtyping remains ambiguous.

Authors

  • Mahmoud M Omran
    Bioinformatics Group, Center for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt.
  • Mohamed Emam
    Bioinformatics Group, Center for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt.
  • Mariam Gamaleldin
    School of Biotechnology, Nile University, Giza, Egypt.
  • Asmaa M Abushady
    School of Biotechnology, Nile University, Giza, Egypt.
  • Mustafa A Elattar
    School of Information Technology and Computer Science, Nile University, Giza, Egypt.
  • Mohamed El-Hadidi
    Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt.

Keywords

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