A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer
Problem
It was unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer, a common and deadly disease.
Why it matters
Colorectal cancer caused over 50,000 US deaths in 2020 and early detection is highly preventable.
What the AI did
A reproducible machine learning framework quantified classification performance using microbiome data annotated at phylum through ASV levels.
Results
Model performance increased with increasing taxonomic resolution up to a level; abstract truncated before the cutoff.
Impact
Findings guide the choice of taxonomic resolution for microbiome-based colorectal cancer detection.
Record
| Venue | mBio |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Courtney R. Armour, Begüm D. Topçuoğlu, Andrea Garretto, Patrick D. Schloss |
| Citations | 35 (OpenAlex, at import time) |
| References | 16 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1128/mbio.03161-21 |
| Open access | gold |
| Keywords | gut microbiome, colorectal cancer, machine learning, taxonomic resolution |
Importance rating
significant — thousands to millions affected
Abstract states colorectal cancer caused over 50,000 US deaths in 2020 and is highly preventable with early detection; the specific problem (optimal taxonomic resolution) is a narrower methodological question within that large disease burden.