Exosome-Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery

Exosomes, small extracellular vesicles (sEVs) secreted by various cell types, play crucial roles in intercellular communication and are increasingly recognized as valuable biomarkers for disease diagnosis and therapeutic targets. Meanwhile, machine learning (ML) techniques have revolutionized biomedical research by enabling the analysis of complex datasets and highly accurate prediction of disease outcomes. Exosomes, with their diverse cargo of proteins, nucleic acids, and lipids, offer a rich source of molecular information reflecting the physiological state of cells. Integrating exosome analysis with ML algorithms, including supervised and unsupervised learning techniques, allows for identifying disease-specific biomarkers and predicting disease outcomes based on exosome profiles. Integrating exosome biology with ML presents a promising avenue for advancing biomedical research and clinical practice. This review explores the intersection of exosome biology and ML in biomedicine, highlighting the importance of integrating these disciplines to advance our understanding of disease mechanisms and biomarker discovery.