ReviewApplied & translational genomics2016
Metabolic modeling with Big Data and the gut microbiome.
Review in Applied & translational genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Characteristics and a comparison of the gut microbiota in two frog species at the beginning and end of hibernation.Frontiers in microbiology · 2023Article
- Article
- Article
- Systems View of Deconditioning During Spaceflight Simulation in the PlanHab Project: The Departure of UrineFrontiers in physiology · 2020Article
- A novel graph theoretical approach for modeling microbiomes and inferring microbial ecological relationships.BMC genomics · 2019Article
- Modelling approaches for studying the microbiome.Nature microbiology · 2019Review
- Article
- Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.Methods (San Diego, Calif.) · 2018Article
- Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis.Nature communications · 2017Article
- Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology.Free radical biology & medicine · 2017Review
- Metabolite and Microbiome Interplay in Cancer Immunotherapy.Cancer research · 2016Review
- MMinte: an application for predicting metabolic interactions among the microbial species in a community.BMC bioinformatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.
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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.