ArticleExperimental & molecular medicine2022
Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects.
Article in Experimental & molecular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Integrated analysis of human-mouse gut microbiota in RSV infection based on machine learning.BMC microbiology · 2026Article
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- Microbial extracellular vesicles in the lung: friends in health, agents in disease.Extracellular vesicles and circulating nucleic acids · 2026Review
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Insight into microbial extracellular vesicles as key communication materials and their clinical implications for lung cancer (Review).International journal of molecular medicine · 2025Review
- Machine learning-assisted construction of COPD self-evaluation questionnaire (COPD-EQ): a national multicentre study in China.Journal of global health · 2025Article
- Examining the bacterial diversity including extracellular vesicles in air and soil: implications for human health.PloS one · 2025Article
- Lung cancer risk prediction using augmented machine learning pipelines with explainable AI.Frontiers in artificial intelligence · 2025Article
- Development and preliminary tests of a portable volumetric capnograph for outpatient use.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2025Article
- The emerging role of bacterial extracellular vesicles in human cancers.Journal of extracellular vesicles · 2024Review
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Extracellular vesicles and COPD: foe or friend?Journal of nanobiotechnology · 2023Review
- Bacterial Extracellular Vesicles: A Candidate Molecule for the Diagnosis and Treatment of Allergic Diseases.Allergy, asthma & immunology research · 2023Review
- Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma.Cancers · 2023Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although mounting evidence suggests that the microbiome has a tremendous influence on intractable disease, the relationship between circulating microbial extracellular vesicles (EVs) and respiratory disease remains unexplored. Here, we developed predictive diagnostic models for COPD, asthma, and lung cancer by applying machine learning to microbial EV metagenomes isolated from patient serum and coded by their accumulated taxonomic hierarchy. All models demonstrated high predictive strength with mean AUC values ranging from 0.93 to 0.99 with various important features at the genus and phylum levels. Application of the clinical models in mice showed that various foods reduced high-fat diet-associated asthma and lung cancer risk, while COPD was minimally affected. In conclusion, this study offers a novel methodology for respiratory disease prediction and highlights the utility of serum microbial EVs as data-rich features for noninvasive diagnosis.
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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.