Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning
importance 5/10
ai assisted
Gut microbiota and health microbiome analytics stage: research
Problem
Characterising the gut microbiome for precision medicine requires extracting insight from complex multi-omics datasets (metagenomics, metatranscriptomics, glycomics, metabolomics).
Why it matters
Microbiome-derived biomarkers could enable personalised diagnosis, prognosis, and treatment of disease.
What the AI did
The paper reviews how machine learning algorithms are applied to extract meaningful insights from complex multi-omics and metagenomics data.
Results
not stated in source
Impact
Review describes ML as a tool for microbiome biomarker identification; no concrete outcomes stated.
Record
| Venue | Frontiers in Molecular Biosciences |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Jingyue Wu, Stephanie S. Singleton, Urnisha Bhuiyan, Lori Krammer, Raja Mazumder |
| Citations | 77 (OpenAlex, at import time) |
| References | 99 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3389/fmolb.2023.1337373 |
| Open access | gold |
| Keywords | gut microbiome, multi-omics, machine learning, precision medicine, metagenomics |
Importance rating
5
real but narrower advance
Microbiome biomarkers could enable personalized diagnosis, prognosis, and treatment, but this is a mini-review with no concrete outcomes or scale stated.