ReviewCurrent medicinal chemistry2025
Exosome-Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery.
Review in Current medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Extracellular Vesicles as Mediators of Pathophysiology and Disease Progression in Cardiovascular Diseases.International journal of molecular sciences · 2026Review
- Engineered small extracellular vesicles in hematologic malignancies: mechanisms, therapeutic strategies, and translational challenges.Clinical and experimental medicine · 2026Review
- Lipid Messengers: Mechanisms and Clinical Applications of Exosomal Lipids in Neurodegenerative Diseases.Molecular neurobiology · 2026Review
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- Advances in the use of exosomes for the diagnosis and treatment of ovarian cancer.World journal of surgical oncology · 2025Review
- Exosomes in Clinical Laboratory: From Biomarker Discovery to Diagnostic Implementation.Medicina (Kaunas, Lithuania) · 2025Review
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Exosomes in Precision Oncology and Beyond: From Bench to Bedside in Diagnostics and Therapeutics.Cancers · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Exosomes, small extracellular vesicles (sEVs) secreted by various cell types, play crucial roles in intercellular communication and are increasingly recognized as valuable biomarkers for disease diagnosis and therapeutic targets. Meanwhile, machine learning (ML) techniques have revolutionized biomedical research by enabling the analysis of complex datasets and highly accurate prediction of disease outcomes. Exosomes, with their diverse cargo of proteins, nucleic acids, and lipids, offer a rich source of molecular information reflecting the physiological state of cells. Integrating exosome analysis with ML algorithms, including supervised and unsupervised learning techniques, allows for identifying disease-specific biomarkers and predicting disease outcomes based on exosome profiles. Integrating exosome biology with ML presents a promising avenue for advancing biomedical research and clinical practice. This review explores the intersection of exosome biology and ML in biomedicine, highlighting the importance of integrating these disciplines to advance our understanding of disease mechanisms and biomarker discovery.
Indexed as
Identifiers
39171463What 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.