생화학분자생물학회

	Name: Sungyong You ( Sungyong.You@csmc.edu )
	2025-present	Professor, Departments of Urology and Computational Biomedicine, Cedars-Sinai Medical Center, USA.
	2023-present	Associate Director, The Center for Artificial Intelligence Research and Education, Cedars-Sinai Medical Center, USA.
	2023-present	Director, Urologic Oncology Bioinformatics, Cedars-Sinai Medical Center, USA
	2022-2025	Associate Professor, Departments of Urology and Computational Biomedicine, Cedars-Sinai Medical Center, USA.
	2017-2022	Assistant Professor, Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, USA.
	2014-2017	Instructor, Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, USA.

Computational frameworks for enhanced extracellular vesicle biomarker discovery

Extracellular vesicles (EVs) are emerging as promising noninvasive biomarkers, yet their clinical translation faces substantial hurdles, primarily due to the challenge of identifying assay-compatible markers. Here, in this Review, we outline sophisticated computational frameworks, particularly leveraging artificial intelligence, to bridge this gap. We detail the integration of diverse data resources, including disease-specific omics, EV, protein localization, tissue-specific, drug, model system and immune databases. This Review comprehensively describes computational selection strategies, from rule-based sequential filtering to advanced machine learning for data fusion and deep learning for multi-omics integration. Crucially, it discusses the refinement of biomarker candidates using artificial-intelligence-driven predictions of protein structure and physicochemical properties, ensuring compatibility with existing assay systems. By systematically evaluating biomarkers for predictive performance, biological plausibility and clinical utility, this framework aims to accelerate the transition of EV research from discovery to clinical application, thereby enhancing precision medicine.


Exp Mol Med. 2026 Feb;58(1):73-81. doi: 10.1038/s12276-025-01622-x.
https://pubmed.ncbi.nlm.nih.gov/41535547/