Evidence map›Paper›PMID 41492373›Full record

ArticleBioscience of microbiota, food and health2026

Predicting autism spectrum disorder through the gut microbiota composition using machine learning.

Dejun Li, Ziyu Huang, Ailing Wei, Kaixian Long, Yunxuan Su, Shiqin Wang, Xing Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioscience of microbiota, food and health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Dejun LiDepartments of Pediatrics, Wuzhou Gongren Hospital, The Seventh Affiliated Hospital of Guangxi Medical University, Wuzhou, Guangxi Zhuang Autonomous Region, China.
Ziyu HuangGuangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Ailing WeiDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Kaixian LongDepartment of Pediatrics, Tiandeng Maternal and Child Health Hospital, Chongzuo, Guangxi Zhuang Autonomous Region, China.
Yunxuan SuDepartments of Pediatrics, The First Affiliated Hospital of Guangxi Medical University/Difficult and Critical illness Center, Pediatric Clinical Medical Research Center of Guangxi, Nanning, Guangxi Zhuang Autonomous Region, China.
Shiqin WangGuangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Xing LiDepartments of Pediatrics, The First Affiliated Hospital of Guangxi Medical University/Difficult and Critical illness Center, Pediatric Clinical Medical Research Center of Guangxi, Nanning, Guangxi Zhuang Autonomous Region, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human gut microbiota plays a crucial role in overall health, impacting various diseases, including autism spectrum disorder (ASD). This study explores the relationship between gut microbiota changes and ASD by analyzing microbial compositions and abundances of 692 gut microbiota samples using public 16S rRNA sequencing datasets. Data preprocessing included normalization and redundancy reduction, retaining 367 microbial features. A machine learning model was then developed to predict ASD, utilizing feature-selected random forest algorithms that showed superior performance in both training and independent test sets. Identified microbial features with high correlation to ASD included

Indexed as

autism spectrum disordergut microbiotamachine learningmicrobial biomarkerspredictive modelingrandom forest

Identifiers

PMID41492373
PMCPMC12765537

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.