Bang, Sohyun, Yoo, DongAhn, Kim, Soo-Jin et al. · Scientific reports · 2019 · DOI
Researchers used artificial intelligence to analyze bacteria in the gut of patients with six different diseases, including ME/CFS, to see if gut bacteria patterns could help identify which disease a person has. They tested different computer learning methods and found that looking at bacteria at the genus level (a specific classification of microorganisms) worked best, and they identified certain bacterial groups that might serve as disease markers.
This research is important for ME/CFS patients because it suggests gut microbiota composition could potentially be used to develop non-invasive diagnostic tests. Understanding which microbial patterns distinguish ME/CFS from other diseases could improve diagnostic accuracy and lead to new therapeutic targets based on microbiome composition.
This study does not prove that gut bacteria cause ME/CFS or that restoring specific bacteria will treat the disease. It is a classification tool study rather than a mechanistic investigation, and the presence of distinct bacterial patterns does not establish whether these differences are a cause or consequence of disease.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Bang, Sohyun, Yoo, DongAhn, Kim, Soo-Jin, Jhang, Soyun, Cho, Seoae, & Kim, Heebal (2019). Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data.. Scientific reports. https://doi.org/10.1038/s41598-019-46249-x
BibTeX
@article{mecfsatlas-bang-2019-establishment-evaluation,
author = {Bang, Sohyun and Yoo, DongAhn and Kim, Soo-Jin and Jhang, Soyun and Cho, Seoae and Kim, Heebal},
title = {Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data.},
journal = {Scientific reports},
year = {2019},
doi = {10.1038/s41598-019-46249-x},
note = {PubMed: 31308384},
url = {https://www.mecfsatlas.com/evidence/bang-2019-establishment-evaluation},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-26. https://www.mecfsatlas.com/evidence/bang-2019-establishment-evaluation
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