ReviewFrontiers in microbiology2024
Deep learning in microbiome analysis: a comprehensive review of neural network models.
Review in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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
32 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
- The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research.Microorganisms · 2026Review
- Robust and Interpretable Metagenomic Modeling Through Structure-Aware Multi-View Learning and Attribution-Guided Biological Insight.Research square · 2026Article
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- Engineering Plant-Associated Soil Microbiomes for Sustainable and Climate-Resilient Agriculture: Mechanisms, Technologies, and Applications.Microorganisms · 2026Review
- Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.Nature communications · 2026Article
- Microbial biobanking: safeguarding the tiny treasures for sustainable human welfare.Folia microbiologica · 2026Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Machine Learning-Guided Synthetic Microbial Communities Enable Functional and Sustainable Degradation of Persistent Environmental Pollutants.Environmental science & technology · 2026Article
- Taxonomic and functional remodeling of the gut microbiota during aging and implications for microbiota-derived biomarkers.World journal of microbiology & biotechnology · 2026Review
- Digital twins for plant-microbe interactions: Gap finding and filling.Plant communications · 2026Article
- Advancing climate-resilient livestock systems: Next-generation emission mitigation strategies and integrated technological innovations.Veterinary and animal science · 2026Review
- Review
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- Emerging Therapeutic Approaches for Modulating the Intestinal Microbiota.Pharmaceutics · 2026Review
- Fecal microbiota transplant and its usefulness in hepatic disorders: a systematic review.Therapeutic advances in gastroenterology · 2026Review
- The evolution of diagnostic microbiology: integrating culture-based methods and genomic advances.PeerJ · 2026Review
- Host-microbiome-immune disequilibrium in oral disease: mechanisms, dysbiosis, and precision therapeutics.Frontiers in immunology · 2026Review
- Artificial intelligence in soil microbiome-driven agriculture: from practical limits to a translational roadmap.Frontiers in microbiomes · 2026Review
- Agentic AI for trustworthy synthetic microbial genomics: a perspective on generation, validation, and governance.Frontiers in bioinformatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.
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.