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Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

Medicine and Health

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

A. Mcdowell, J. Kang, et al.

This groundbreaking study reveals high-performing predictive models for COPD, asthma, and lung cancer using machine learning on microbial extracellular vesicle metagenomes from patient serum. Conducted by Andrea McDowell and her team, the research proposes serum microbial EVs as noninvasive diagnostic features with remarkable accuracy.

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Playback language: English
Abstract
This study developed predictive diagnostic models for COPD, asthma, and lung cancer using machine learning on microbial extracellular vesicle (EV) metagenomes from patient serum. All models showed high predictive strength (AUC 0.93-0.99). In mice, various foods reduced high-fat diet-associated asthma and lung cancer risk, while COPD was minimally affected. Serum microbial EVs are proposed as data-rich features for noninvasive diagnosis.
Publisher
Experimental & Molecular Medicine
Published On
Sep 30, 2022
Authors
Andrea McDowell, Juwon Kang, Jinho Yang, Jihee Jung, Yeon-Mok Oh, Sung-Min Kym, Tae-Seop Shin, Tae-Bum Kim, Young-Koo Jee, Yoon-Keun Kim
Tags
COPD
asthma
lung cancer
machine learning
microbial extracellular vesicles
diagnostic models

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