Comparative analysis of statistical and deep learning-based multi-omics integration for breast cancer subtype classification
These findings suggest that MOFA+ is a more effective unsupervised tool for feature selection in BC subtyping. Our study underscores the potential of multi-omics integration to improve BC subtype prediction and provides critical insights for advancing personalized medicine in BC.
Authors
- Mohamed M. Omran
- Asmaa M. Abushady (ORCID: https://orcid.org/0000-0001-9906-3578)
- Mustafa Elattar (ORCID: https://orcid.org/0000-0001-7936-3522)
- Mariam Gamaleldin (ORCID: https://orcid.org/0000-0001-6281-7102)
- Mohamed El-Hadidi (ORCID: https://orcid.org/0000-0002-3593-8061)
- Mohamed Emam
Institutions
- University of Dubai (AE)
- Universidade do Porto (PT)
- Nile University (EG)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1186/s12967-025-06662-5
- Citations
- 6
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 9.11
Funders
- Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona