Network Analysis in Microbiome Research: Methods, Tools, and Applications.

Jiajun Liu, Yixue Li, Tao Huang

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Jiajun LiuDepartment of Artificial Intelligence and Digital Health, CAS Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.

Yixue LiDepartment of Artificial Intelligence and Digital Health, CAS Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. li_yixue@gzlab.ac.cn.

Tao HuangDepartment of Artificial Intelligence and Digital Health, CAS Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. huangtao@sinh.ac.cn.ORCID https://orcid.org/0000-0003-1975-9693

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human microbiome operates as a complex, interconnected ecosystem where microbial interactions dictate community stability, host health, and disease progression. Understanding these dynamics requires moving beyond simple taxonomic catalogs to systems-level network analyses. This chapter reviews network methodologies in microbiome research, progressing from traditional correlation-based approaches to advanced artificial intelligence techniques. We systematically cover co-occurrence, protein-protein interaction, metabolic, multi-omics integrated, and evolutionary transmission networks. Computational tools-spanning general platforms and specialized pipelines-are compared alongside topology metrics and community detection algorithms. Furthermore, we highlight the integration of graph neural networks and protein language models, discussing current challenges in data standardization, model interpretability, and the merging of mechanistic and data-driven paradigms.

Indexed as

Computational BiologyMicrobiotaAlgorithmsGraph Neural NetworksHumansMultiomicsCommunity detectionGraph neural networksMicrobiome network analysisMulti-omics integrationNetwork topologyViral evolution networks

Identifiers

PMID42108293

DOI10.1038/s41467-025-64175-7

What OpenQuestion holds

Textmetadata

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.