ArticleFrontiers in microbiology2023
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Immunotherapy with guts: a review of microbial therapeutic adjuncts for immunotherapy in solid tumors.The oncologist · 2026Review
- Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Large-scale classification of metagenomic samples: a comparative analysis of classical machine learning techniques vs a novel brain-inspired hyperdimensional computing approach.bioRxiv : the preprint server for biology · 2025Article
- Review
- Assessing the safety of microbiome perturbations.Microbial genomics · 2025Review
- Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome.Journal of clinical medicine · 2025Review
- Machine learning models reveal Saccharomyces yeasts are associated with poor piglet growth in early development.Journal of animal science · 2025Article
- Review
- Human intestinal microbiome: Role in health and disease.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2024Review
- Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina.Diagnostics (Basel, Switzerland) · 2024Article
- AI in microbiome-related healthcare.Microbial biotechnology · 2024Review
- Microbiome-Based Colon Cancer Patient Stratification and Survival Analysis.Cancer medicine · 2024Article
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Multi-class boosting for the analysis of multiple incomplete views on microbiome data.BMC bioinformatics · 2024Article
- Exploring Cereal Metagenomics: Unravelling Microbial Communities for Improved Food Security.Microorganisms · 2024Review
- MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information.Frontiers in microbiology · 2024Article
- Overview of data preprocessing for machine learning applications in human microbiome research.Frontiers in microbiology · 2023Review
Corrections and comments
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Authors and funding
41 authors.
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Abstract
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
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