ReviewFrontiers in microbiology2024
A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.
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, 2 of them syntheses 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, 2 syntheses or guidelines pooled it.
- Systematic Review: Long-Read Sequencing in Algal Studies.International journal of molecular sciences · 2026Pooled it
- Impact of non-standardized reporting on reproducibility, usability, and integration in nasopharyngeal metagenomic research: a systematic review.Frontiers in microbiology · 2026Pooled it
- Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.Nature communications · 2026Article
- IDBac: an open-access web platform to identify bacteria and analyze relationships in culture collections using MALDI-TOF mass spectrometry.Nature communications · 2026Article
- Challenges and Solutions in Low-Biomass Respiratory Microbiome Profiling: A Workflow for Bronchoalveolar Lavage Fluid Sequencing in Guinea Pigs.Physiological research · 2026Article
- Urinary Microbiome Characteristics in Kidney Transplant Recipients and Their Clinical Implications: A Narrative Review.Annals of transplantation · 2026Review
- Fungal ecology in the age of 'omics.The New phytologist · 2026Review
- The gut-heart axis in atrial fibrillation: Pathophysiology, evidence, and therapeutic potential.Heart rhythm O2 · 2026Review
- Standardized and batch effect-independent technologies enable global collaboration in microbiome research.The ISME journal · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- A realistic simulation-based benchmark of microbiome normalization in sample stratification and taxa-level analysis.Frontiers in bioinformatics · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Gut microbiota-derived metabolites in cardiovascular disease: mechanisms, disease-specific roles, and translational opportunities.Frontiers in cardiovascular medicine · 2026Review
- IDBac: an open-access web platform and compendium for the identification of bacteria by MALDI-TOF mass spectrometry.bioRxiv : the preprint server for biology · 2025Article
- Gut as a Target of Ochratoxin A: Toxicological Insights and the Role of Microbiota.International journal of molecular sciences · 2025Review
- Identifying Optimal Machine Learning Approaches for Human Gut Microbiome (Shotgun Metagenomics) and Metabolomics Integration with Stable Feature Selection.bioRxiv : the preprint server for biology · 2025Article
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Do we need a standardized 16S rRNA gene amplicon sequencing analysis protocol for poultry microbiota research?Poultry science · 2025Review
- Engineering Useful Microbial Species for Pharmaceutical Applications.Microorganisms · 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
No grant is acknowledged in the PubMed record.
Abstract
Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.
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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.