Recent advances in machine learning-enhanced extracellular vesicle omics for oncology - PubMed
6 hours ago
- #precision oncology
- #extracellular vesicles
- #machine learning
- Extracellular vesicles (EVs) are nanoscale particles carrying biomolecules, useful as liquid biopsy biomarkers for oncology.
- Machine learning (ML) can analyze high-dimensional EV omics data (transcriptomics, proteomics, metabolomics, lipidomics) for cancer detection, subtyping, prognosis, and treatment response.
- Challenges include EV heterogeneity, isolation variability, and co-isolation, which hinder clinical translation.
- Key issues involve data quality, model generalizability, interpretability, ethics, and standardization, with recommendations for study design.
- Emerging directions are single-vesicle omics, higher-resolution profiling, interpretable multimodal fusion, and end-to-end ML-EV multi-omics pipelines.