A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification

This work emphasizes the importance of moving to multi-label subtyping to capture all the molecular traits of individual patients, considering also previously overlooked secondary assignments and paving the way for improved clinical decision-making processes in diverse heterogeneous disease contexts. Indeed, MULTI-STAR novel, reproducible and generalizable approach provides comprehensive representations of patient inner heterogeneity and clinically relevant insights, contributing to precision medicine and personalized treatments.