ReviewFrontiers in microbiology2023
Machine learning approaches in microbiome research: challenges and best practices.
Review 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 68 papers.
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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.
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Who cites it
68 citing papers in PubMed.
- The role of gut microbiome in aging-associated diseases: where do we stand now and how technology will transform the future.Gut microbes · 2026Review
- Evaluating Vaginal Microbiome Effects on Antiretroviral Exposure in the Female Genital Tract Using Pharmacometric and Machine Learning Approaches.Clinical pharmacology and therapeutics · 2026Article
- Seed Biopriming for Climate Stress Resilience: Molecular, Physiological, and Epigenetic Mechanisms.International journal of molecular sciences · 2026Review
- Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.Die Naturwissenschaften · 2026Article
- Identification of Key Microbial Signatures Associated With Breast Cancer: Machine Learning-Based Approaches Using Gut Microbiome Data.Cancer medicine · 2026Article
- Taxonomic and functional remodeling of the gut microbiota during aging and implications for microbiota-derived biomarkers.World journal of microbiology & biotechnology · 2026Review
- Anxiety associated with dietary intake and gut microbiome features in a cross-sectional cohort of sub-clinically anxious young women.medRxiv : the preprint server for health sciences · 2026Article
- A Systems Approach to Endophyte-Mediated Plant Holobiont and Microbiome Dynamics.Plants (Basel, Switzerland) · 2026Review
- Interplay of Microbiome, Oxidative Stress and Inflammation in Health and Disease.Antioxidants (Basel, Switzerland) · 2026Review
- Emerging Therapeutic Approaches for Modulating the Intestinal Microbiota.Pharmaceutics · 2026Review
- Article
- Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- Early-life microbiome trajectories as biomarkers to predict health outcomes.Microbiome research reports · 2026Review
- Predicted meta-omics: A potential solution to multi-omics data scarcity in microbiome studies.PloS one · 2026Article
- Gi-MAPS: a quantitative engineering framework for AI-guided pediatric gut microbiome ecological interpretation and digital-twin simulation.Frontiers in microbiology · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- Predictive modeling for cervical cancer: existing AI approaches and the emerging role of vaginal microbiome.Frontiers in network physiology · 2026Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.
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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.