ReviewEmerging topics in life sciences2021
It takes guts to learn: machine learning techniques for disease detection from the gut microbiome.
Review in Emerging topics in life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
- ATOMIC: a graph attention network for atopic dermatitis prediction using human gut microbiome.Frontiers in immunology · 2025Article
- Gut microbiota analysis reveals microbial signature for multi-autoimmune diseases based on machine learning model.Frontiers in microbiology · 2025Article
- Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies.Frontiers in microbiology · 2025Article
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Establishment of a non-Westernized gut microbiota in men who have sex with men is associated with sexual practices.Cell reports. Medicine · 2024Article
- Reference-free Structural Variant Detection in Microbiomes via Long-read Coassembly Graphs.bioRxiv : the preprint server for biology · 2024Article
- Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples.Veterinary sciences · 2023Review
- GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data.Bioinformatics (Oxford, England) · 2023Article
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
- Xylan Prebiotics and the Gut Microbiome Promote Health and Wellbeing: Potential Novel Roles for Pentosan Polysulfate.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.