A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification.
Journal:
Journal of biomedical informatics
Published Date:
Apr 9, 2025
Abstract
OBJECTIVES: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simplify patient molecular portraits, neglecting the potential co-occurrence of traits from multiple subtypes. Yet, recognizing intra-sample heterogeneity is essential for more precise patient characterization and improved personalized treatments.