ReviewMicrobial genomics2024
Deep learning methods in metagenomics: a review.
Review in Microbial genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- 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
- Wastewater Metagenomics for Antimicrobial Resistance and Pathogen Surveillance: A Bibliometric Analysis.Microorganisms · 2026Review
- Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Benchmarking short- and long-read sequencing technologies for metagenomic profiling of microbiomes.Scientific reports · 2026Article
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- The gut as a central hub for multi-organ crosstalk in aging.Cellular and molecular life sciences : CMLS · 2026Review
- A Systems Approach to Endophyte-Mediated Plant Holobiont and Microbiome Dynamics.Plants (Basel, Switzerland) · 2026Review
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Integrating artificial intelligence with genome sequencing against antimicrobial resistance: a narrative review.Frontiers in public health · 2026Review
- DeepTaxa: a hybrid CNN-BERT framework for 16S rRNA taxonomic classification.Bioinformatics advances · 2026Article
- Quantifying human-environment interactions through Bayesian modeling of species-resolved microbial transfer signatures: an exploratory proof-of-concept study.Frontiers in microbiology · 2026Article
- Charting the virosphere: computational synergies of AI and bioinformatics in viral discovery and evolution.Journal of virology · 2025Review
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- Engineered probiotics: a new era in treating inflammatory bowel disease.Journal of translational medicine · 2025Review
- Virome of canine lymph nodes: identification of viruses with zoonotic potential.Virology journal · 2025Article
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
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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.