ReviewFrontiers in cellular and infection microbiology2026
Design, processing, and modeling for longitudinal multiomics microbiome data.
Review in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
Longitudinal multiomics studies can reveal mechanisms underlying microbiome dynamics. Though gathering such data has become increasingly accessible, challenges remain in experimental design, data processing, and interaction modeling. This mini-review surveys practical approaches for analyzing longitudinal multiomics microbiome data. We provide an overview of fundamental questions these experimental designs can address, discuss concepts for reducing confounding, review tools for data management, and describe statistical and machine learning methods for identifying interactions across time and biological layers. We conclude with emerging trends and open problems.
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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.