ReviewmBio2024
Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology.
Review in mBio, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- High dietary BGut microbes · 2026Article
- Article
- Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.Applied microbiology and biotechnology · 2026Review
- Article
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- A workflow for statistical analysis and visualization of microbiome omics data using the R microeco package.Nature protocols · 2026Review
- Cascade Dams and Seasonality Jointly Structure Gut Microbiome Biogeography inMicroorganisms · 2026Article
- Methane cycling microbes are important predictors of methylmercury accumulation in rice paddies.Applied and environmental microbiology · 2026Article
- Interplay of Microbiome, Oxidative Stress and Inflammation in Health and Disease.Antioxidants (Basel, Switzerland) · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Review
- Artificial intelligence in soil microbiome-driven agriculture: from practical limits to a translational roadmap.Frontiers in microbiomes · 2026Review
- Advances in Molecular Microbiology-From Recent Advances to the Future.Current issues in molecular biology · 2025Article
- Review
- Community-Level Metabolic Shifts Following Land Use Change in the Amazon Rainforest Identified by a Supervised Machine Leaning Approach.Environmental microbiology reports · 2025Article
- Review
- Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease.Diagnostics (Basel, Switzerland) · 2024Article
- Leveraging genomic information to predict environmental preferences of bacteria.The ISME journal · 2024Review
- A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.Frontiers in microbiology · 2024Review
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.
Funding
Abstract
Due to the complex nature of microbiome data, the field of microbial ecology has many current and potential uses for machine learning (ML) modeling. With the increased use of predictive ML models across many disciplines, including microbial ecology, there is extensive published information on the specific ML algorithms available and how those algorithms have been applied. Thus, our goal is not to summarize the breadth of ML models available or compare their performances. Rather, our goal is to provide more concrete and actionable information to guide microbial ecologists in how to select, run, and interpret ML algorithms to predict the taxa or genes associated with particular sample categories or environmental gradients of interest. Such microbial data often have unique characteristics that require careful consideration of how to apply ML models and how to interpret the associated results. This review is intended for practicing microbial ecologists who may be unfamiliar with some of the intricacies of ML models. We provide examples and discuss common opportunities and pitfalls specific to applying ML models to the types of data sets most frequently collected by microbial ecologists.
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.