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Exp. Mol. Med., 14 January 2026 | https://doi.org/10.1038/s12276-025-01622-x Computational frameworks for enhanced extracellular vesicle biomarker discovery

Jina Kim ^1,2, Ju Dong Yang ^3,4,5, Vatche G. Agopian ^6,7, Yazhen Zhu ^7,8,9, Hsian-Rong Tseng ^7,9and Sungyong You ^1,2,3,*

¹Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
²Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
³Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁴Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁵Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁶Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁷Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA.
⁸Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁹Department of Molecular and Medical Pharmacology, California Nanosystems Institute, Crump Institute for Molecular Imaging, University of California, Los Angeles, Los Angeles, CA, USA.

^*Corresponding author: correspondence to Sungyong You

Abstract

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.

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