SynthesisBriefings in bioinformatics2025
Multi-omics time-series analysis in microbiome research: a systematic review.
Synthesis in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Microbial-driven nature-based solutions for environmental antimicrobial resistance and emerging contaminants: mechanisms, platform trade-offs, and decision framework.Frontiers in microbiology · 2026Pooled it
- Gut microbiome and metabolic health: mechanisms and precision interventions.Gut microbes · 2026Review
- Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges.Archives of microbiology · 2026Review
- Review
- Taxonomic and functional remodeling of the gut microbiota during aging and implications for microbiota-derived biomarkers.World journal of microbiology & biotechnology · 2026Review
- Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability.Briefings in bioinformatics · 2026Review
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- The microbial metabolome: remodeling the therapeutic landscape in hematologic malignancies.NPJ biofilms and microbiomes · 2026Review
- Article
- Programming the tumor microenvironment through microbiome-driven mechanisms.Frontiers in cellular and infection microbiology · 2026Review
- Host-microbiome interactions in leukemia: mechanisms, treatment response, and clinical implications.Frontiers in cellular and infection microbiology · 2026Review
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Probiotics in irritable bowel syndrome: strain-specific effects, diet, and biomarker timing.Frontiers in cellular and infection microbiology · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
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
Recent developments in data generation have opened up unprecedented insights into living systems. It has been recognized that integrating and characterizing temporal variation simultaneously across multiple scales, from specific molecular interactions to entire ecosystems, is crucial for uncovering biological mechanisms and understanding the emergence of complex phenotypes. With the increasing number of studies incorporating multi-omics data sampled over time, it has become clear that integrated approaches are pivotal for these efforts. However, standard data analytical practices in longitudinal multi-omics are still shaping up and many of the available methods have not yet been widely evaluated and adopted. To address this gap, we performed the first systematic literature review that comprehensively categorizes, compares, and evaluates computational methods for longitudinal multi-omics integration, with a particular emphasis on four categories of the studies: (i) host and host-associated microbiome studies, (ii) microbiome-free host studies, (iii) host-free microbiome studies, and (iv) methodological framework studies. Our review highlights current methodological trends, identifies widely used and high-performing frameworks, and assesses each method across performance, interpretability, and ease of use. We further organize these methods into thematic groups-such as statistical modeling, machine learning, dimensionality reduction, and latent factor approaches-to provide a clear roadmap for future research and application. This work offers a critical foundation for advancing integrative longitudinal data science and supporting reproducible, scalable analysis in this rapidly evolving field.
Indexed as
Identifiers
What Socratic 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 Socratic graph.