It takes guts to learn: machine learning techniques for disease detection from the gut microbiome

Kristen D. Curry, Michael G. Nute, Todd J. Treangen

Emerging Topics in Life Sciences 2021 article cited by 30

Source Open-access PDF More in Medicine & Health

importance 5/10 partially solved Gut microbiota and health microbiome diagnostics stage: research

Problem

Predicting human diseases from gut microbiome data is difficult, with ML showing promise for some conditions but lacking efficacy for others.

Why it matters

Non-invasive microbiome-based detection could enable earlier diagnosis of conditions from liver cirrhosis to neurological deficits.

What the AI did

Reviews machine learning methods for disease classification from gut metagenomic data, highlighting computational challenges overcome and biological components overlooked.

Results

not stated in source

Impact

Evidence indicates ML is promising for some diseases (e.g., liver cirrhosis, IBD) but lacks efficacy for others.

Record

Venue	Emerging Topics in Life Sciences
Published	2021
Work type	article
Authors	Kristen D. Curry, Michael G. Nute, Todd J. Treangen
Citations	30 (OpenAlex, at import time)
References	129
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1042/etls20210213
Open access	hybrid
Keywords	gut microbiome, machine learning, disease detection, metagenomics, review

Importance rating

5 / 10

real but narrower advance

Non-invasive microbiome-based disease prediction could aid diagnosis of conditions from liver cirrhosis to neurological deficits, but the abstract reports mixed efficacy and is a review without results.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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