Evidence map›Paper›PMID 38058764›Full record

ReviewMicrobiome research reports2023

Connect the dots: sketching out microbiome interactions through networking approaches.

Marco Fabbrini, Daniel Scicchitano, Marco Candela, Silvia Turroni, Simone Rampelli

Abstract readReview
In one paragraph

Review in Microbiome research reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025
    Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Marco FabbriniMicrobiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy.
Daniel ScicchitanoUnit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy.
Marco CandelaUnit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy.
Silvia TurroniUnit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy.
Simone RampelliUnit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment.

Indexed as

community modelingecological networksMicrobial networksnetwork approachesnetwork interactionnetwork topologyshotgun metagenomics

Identifiers

PMID38058764
PMCPMC10696587

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.