Evidence map›Paper›PMID 38117560›Full record

ArticleGut microbes

The impact of gut microbiome enterotypes on ulcerative colitis: identifying key bacterial species and revealing species co-occurrence networks using machine learning.

Xuangao Wu, Ting Zhang, TianShun Zhang, Sunmin Park

Open access · goldAbstract read
In one paragraph

Article in Gut microbes. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 2 pooled it
8.5field-weighted citation impact, top 2% of its field
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

37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Bridging the gap:Gut microbes · 2026
    Review
  4. Gut microbiota in health and disease.Molecular biomedicine · 2026
    Review
  5. Review
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  13. SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025
    Article
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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

4 authors at 1 institution in 1 country.

Xuangao WuDepartment of Bioconvergence, Hoseo University, Asan, Korea.
Ting ZhangDepartment of Bioconvergence, Hoseo University, Asan, Korea.
TianShun ZhangDepartment of Food and Nutrition, Obesity/Diabetes Research Center, Hoseo University, Asan, Korea.
Sunmin ParkDepartment of Bioconvergence, Hoseo University, Asan, Korea.ORCID 0000-0002-6092-8340
Hoseo University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ulcerative colitis (UC) is a chronic inflammatory intestinal disease affecting the colon and rectum, with its pathogenesis attributed to genetic background, environmental factors, and gut microbes. This study aimed to investigate the role of enterotypes in UC by conducting a hierarchical analysis, determining differential bacteria using machine learning, and performing Species Co-occurrence Network (SCN) analysis. Fecal bacterial data were collected from UC patients, and a 16S rRNA metagenomic analysis was performed using the QIIME2 bioinformatics pipeline. Enterotype clustering was conducted at the family level, and deep neural network (DNN) classification models were trained for UC and healthy controls (HC) in each enterotype. Results from eleven 16S rRNA gut microbiome datasets revealed three enterotypes: Bacteroidaceae (ET-B), Lachnospiraceae (ET-L), and Clostridiaceae (ET-C).

Indexed as

Clostridioides difficileColitis, UlcerativeGastrointestinal MicrobiomeBacteriaBacteroidaceaeHumansMachine LearningRNA, Ribosomal, 16SRNA, Ribosomal, 16SB. uniformisco-occurrence networkenterotypesgut microbiomemachine learningO. splanchnicusR. gnavusUlcerative colitis

Identifiers

PMID38117560
PMCPMC10761161
OpenAlexW4389991985

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.