Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology

Extracellular vesicles (EVs) are emerging as naturally bioactive nanomaterials with intrinsic biocompatibility and targeting potential. Recent integration of machine learning (ML) into EV research has accelerated advances in molecular profiling, structure-function prediction, and rational design of vesicle-based therapeutics. Yet, the inherent complexity and heterogeneity of EV populations pose major analytical challenges. Concurrently, machine learning is revolutionizing biomedical science by uncovering patterns in high dimensional, multimodal datasets. In EV research, ML has enabled major advances across automated imaging, multi omics integration, disease classification, therapeutic engineering, and…

• NS
  National Science and Technology Council

  Award: NSTC 113-2113-M-038-001
• TM
  Taipei Medical University