ReviewComputational and structural biotechnology journal2025
Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical Applications.Current issues in molecular biology · 2026Review
- Engineered small extracellular vesicles in hematologic malignancies: mechanisms, therapeutic strategies, and translational challenges.Clinical and experimental medicine · 2026Review
- Review
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- Bioengineering of extracellular vesicles with scaffold proteins for drug delivery.Journal of nanobiotechnology · 2026Review
- Exploring the Role of Extracellular Vesicles in Pancreatic and Hepatobiliary Cancers: Advances Through Artificial Intelligence.International journal of molecular sciences · 2026Review
- Advanced Technologies in Extracellular Vesicle Biosensing: Platforms, Standardization, and Clinical Translation.Molecules (Basel, Switzerland) · 2026Review
- Exosome diagnostics beyond the hype: why study design still matters.International journal of surgery (London, England) · 2026Article
- ExoOrb: A novel visual and analytical system for therapeutic extracellular vesicles metrics.Computational and structural biotechnology journal · 2025Article
- Conditioned Media-Derived Tumor Extracellular Vesicles: Bridging Molecular Insights and Therapeutic Applications in Oncology.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine for tumors represents a pivotal focus in contemporary medical research. Nonetheless, the diversity of tumor types and the complexity of their pathogenesis present significant challenges in the diagnostic process. Extracellular vesicles (EVs), as a category of nanoparticles, carry a wealth of biological information and play a crucial role in tumor initiation and progression, thereby offering novel approaches for early tumor diagnosis. In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. This review provides a comprehensive summary of advancements in EVs analysis technology based on ML for auxiliary tumor diagnosis, including early diagnosis, classification, stage recognition, and molecular diagnosis, and discusses their advantages in clinical applications. Additionally, the article anticipates future development trends in the field, aiming to serve as a reference for researchers engaged in ML-assisted liquid biopsy for tumor diagnosis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.