Computational frameworks for enhanced extracellular vesicle biomarker discovery
Cedars-Sinai Medical Center · University of California, Los Angeles · +1 more institution
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…
Citation impact
- FWCI
- 45.66
- Percentile
- 100%
- References
- 84
Authors
6- JKJina Kim
Cedars-Sinai Medical Center
- JDJu Dong Yang
Cedars-Sinai Medical Center
- VGVatche G. Agopian
University of California, Los Angeles
- YZYazhen Zhu
California NanoSystems Institute, University of California, Los Angeles
- HTHsian‐Rong Tseng
California NanoSystems Institute, University of California, Los Angeles
Topics & keywords
- Extracellular vesicles
- Biomarker discovery
- Computational model
- Extracellular vesicle
- Biomarker
- Drug discovery
- Biomedicine