Evidence map›Paper›PMID 41675159›Full record

ArticleiMetaOmics2025

Transfer learning identifies bacterial signatures for cross-regional diagnosis of type 2 diabetes and enable stage-sensitive dietary fiber intervention.

Qunye Zhang, Nan Wang, Fanghua Zhang, Bin Chen, Yihui Wang, Zhongchao Wang, Changying Zhao, Chuandi Jin, Dashuang Sheng, Kaile Yue and 17 more

Abstract readLetter
PubMed Full text at PMC Publisher
In one paragraph

Article in iMetaOmics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. From trees to graphs: rethinking phylogeny in microbiome prediction.Applied and environmental microbiology · 2026
    Article
  2. Article
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

27 authors.

Qunye ZhangMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Nan WangKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Fanghua ZhangDepartment of Endocrinology, Qingdao Central Hospital University of Health and Rehabilitation Sciences (Qingdao Central Hospital) Qingdao China.
Bin ChenMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Yihui WangMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Zhongchao WangQingdao University Medical Group: Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University; Department of Endocrinology and Metabolism, The Third People's Hospital of Qingdao Qingdao China.
Changying ZhaoMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Chuandi JinMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Dashuang ShengMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Kaile YueMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Daifeng JiangShandong Academy of Sciences Science and Technology Services Platform (Shandong Academy of Sciences Foreign Students Pioneer Park) Jinan China.
Liaomei GaoShandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University Jinan China.
Haohong ZhangKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.ORCID 0000-0001-6267-4244
Zixin KangKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Mingyue ChengKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Xiaoli MaDepartment of Endocrinology Qingdao Municipal Hospital (Group) Qingdao China.
Haiyan WangDepartment of Endocrinology The Eighth People's Hospital of Qingdao Qingdao China.
Dongming HuQingdao University Medical Group: Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University; Department of Endocrinology and Metabolism, The Third People's Hospital of Qingdao Qingdao China.
Jun WangQingdao University Medical Group: Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University; Department of Endocrinology and Metabolism, The Third People's Hospital of Qingdao Qingdao China.
Yuantao LiuDepartment of Endocrinology Qilu Hospital of Shandong University (Qingdao) Qingdao China.
Chenhong ZhouDepartment of Endocrinology Qingdao Municipal Hospital (Group) Qingdao China.
Minxiu YaoDepartment of Endocrinology, Qingdao Central Hospital University of Health and Rehabilitation Sciences (Qingdao Central Hospital) Qingdao China.
Guoping ZhaoMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.
Yangang WangQingdao University Medical Group: Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University; Department of Endocrinology and Metabolism, The Third People's Hospital of Qingdao Qingdao China.
Zhe WangDepartment of Endocrinology & Geriatrics Shandong Provincial Hospital Affiliated to Shandong First Medical University Jinan China.
Kang NingKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Lei ZhangMicrobiome-X, School of Public Health; Department of Cardiology, Qilu Hospital; Cheeloo College of Medicine Shandong University Jinan China.ORCID 0000-0003-4284-2604

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DeepMicroFinder is a deep learning framework designed to update the existing disease diagnosis model to generate a transfer model by leveraging region-specific microbiome datasets and transfer learning approach. This framework effectively overcomes the limitation of regional effects in the gut microbiome, enabling accurate cross-regional disease detection. Microbial markers related to type 2 diabetes (T2D) were identified by DeepMicroFinder, and subsequently validated in independent T2D cohorts undergoing dietary fiber interventions.

Identifiers

PMID41675159
PMCPMC12806279

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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.