ArticleJournal of microbiology (Seoul, Korea)2020
Machine learning methods for microbiome studies.
Article in Journal of microbiology (Seoul, Korea), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Straw return is associated with improved morel cultivation performance and with changes in soil quality and microbial communities.World journal of microbiology & biotechnology · 2026Article
- Article
- Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction.Bioinformatics (Oxford, England) · 2026Article
- Diversity and random forest models of oral microbiomes in periodontal health using publicly available data.Journal of periodontology · 2026Article
- A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.Computational and structural biotechnology journal · 2026Article
- 16S ribosomal ribonucleic acid sequencing reveals bile microbiome features in gallstone disease and their links to blood lipid subtypes.Frontiers in microbiology · 2026Article
- A case study of ChatGPT-assisted building of a microbiome-based machine learning model for biologists.Journal of microbiology & biology education · 2025Article
- Integrating Microbiomes for Regenerative Food Systems: Recent Insights, Implementations, and Emerging Trends.Food science & nutrition · 2025Review
- Article
- Optimizing breast cancer chemotherapy by harnessing gut microbiota with insights from artificial intelligence.NPJ biofilms and microbiomes · 2025Review
- Characterization of vaginal prokaryotes and eukaryotes microbiota and its link to sexually transmitted infections.Scientific reports · 2025Article
- Machine learning as an artificial intelligence application in management of chronic hepatitis B virus infection.World journal of gastroenterology · 2025Article
- Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (Foods (Basel, Switzerland) · 2025Article
- Differential intestinal microbiome response to heat stress in two rabbit maternal lines: a comparative analysis using Random Forest, BayesC, and PLS-DA.Journal of animal science · 2025Article
- Advancing time-since-interval estimation for clandestine graves: a forensic ecogenomics perspective into burial and translocation timelines using massively parallel sequencing.Frontiers in microbiology · 2025Review
- micRoclean: an R package for decontaminating low-biomass 16S-rRNA microbiome data.Frontiers in bioinformatics · 2025Article
- Article
- Characteristics of gut microbiota of premature infants in the early postnatal period and their relationship with intraventricular hemorrhage.BMC microbiology · 2024Article
- Baseline gut microbiota diversity and composition and albendazole efficacy in hookworm-infected individuals.Parasites & vectors · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the development of sequencing technology, microbiome studies with large number of samples are eligible on an acceptable cost nowadays. Large samples allow analysis of more sophisticated modeling using machine learning approaches to study relationships between microbiome and various traits. This article provides an overview of machine learning methods for non-data scientists interested in the association analysis of microbiomes and host phenotypes. Once genomic feature of microbiome is determined, various analysis methods can be used to explore the relationship between microbiome and host phenotypes that include penalized regression, support vector machine (SVM), random forest, and artificial neural network (ANN). Deep neural network methods are also touched. Analysis procedure from environment setup to extract analysis results are presented with Python programming language.
Indexed as
Identifiers
32108316What 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.