ReviewGenes2023
Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review.
Review in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- The role of the microbiota in hematological malignancies: A narrative review of mechanisms and therapeutic potential.New microbes and new infections · 2026Review
- Article
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.Biostatistics (Oxford, England) · 2026Article
- Evaluating preservation effects on honeybee gut microbiota inocula.Microbiology spectrum · 2026Article
- Integrative artificial intelligence and multi-omics modeling approach for characterizing microbial dynamics and health impacts in space microgravity and radiation conditions.Frontiers in microbiology · 2026Article
- Longitudinal integration of microbiota and metabolomics reveals (poly)phenols-driven gut ecosystem dynamics.Frontiers in nutrition · 2026Article
- Comparative analysis of gut microbiota and host phenotypic characteristics across enterotype-like clusters in cynomolgus and rhesus macaques.Frontiers in microbiology · 2026Article
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
- Design, processing, and modeling for longitudinal multiomics microbiome data.Frontiers in cellular and infection microbiology · 2026Review
- Application and Challenges of Using ProbioticDiseases (Basel, Switzerland) · 2025Review
- Decoding longitudinal microbiome trajectories: an interpretable machine learning approach for biomarker discovery and prediction.Briefings in bioinformatics · 2025Article
- Article
- Modulation of the Neuro-Cancer Connection by Metabolites of Gut Microbiota.Biomolecules · 2025Review
- Unlocking the secrets of the human gut microbiota: Comprehensive review on its role in different diseases.World journal of gastroenterology · 2025Review
- The impact of perinatal maternal stress on the maternal and infant gut and human milk microbiomes: A scoping review.PloS one · 2025Article
- Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease.Journal of inflammation research · 2025Review
- UniCor and UniCorP: a novel metric and hierarchical feature selection algorithm for microbial community analysis.ISME communications · 2025Article
- Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes.bioRxiv : the preprint server for biology · 2024Article
- Microbial Gatekeepers of Fertility in the Female Reproductive Microbiome of Cattle.International journal of molecular sciences · 2024Review
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
Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.
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.