Evidence map›Paper›PMID 40131311›Full record

ArticleBriefings in bioinformatics2025

MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.

Jie Ni, Bin Li, Shumei Miao, Xinting Zhang, Donghui Yan, Shengqi Jing, Shan Lu, Zhuoying Xie, Xin Zhang, Yun Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

10 authors.

Jie NiInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2, Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102, China.
Bin LiInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2, Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102, China.
Shumei MiaoDepartment of Information, The First Affiliated Hospital, Nanjing Medical University, No. 300, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China.
Xinting ZhangInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2, Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102, China.
Donghui YanInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2, Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102, China.
Shengqi JingDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, No. 101, Longmian Avenue, Jiangning District, Nanjing, Jiangsu 211166, China.
Shan LuWomen and Children Department, The First Affiliated Hospital, Nanjing Medical University, No. 300, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China.
Zhuoying XieInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2, Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102, China.ORCID 0000-0003-3534-1924
Xin ZhangDepartment of Information, The First Affiliated Hospital, Nanjing Medical University, No. 300, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China.
Yun LiuDepartment of Information, The First Affiliated Hospital, Nanjing Medical University, No. 300, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China.

Funding

Fundamental Research Funds for the Central Universities of China 2242023 K5005Ministry of Science and Technology of the People's Republic of China National Key Research & Development Program 2023YFC3605800Nanjing Science and Technology Bureau Project 202205053Natural Science Foundation of Jiangsu Province BK20222008SEU Innovation Capability Enhancement Plan for Doctoral Students CXJH_SEU 25145Social Development Plan of the Provincial Department of Science and Technology in Jiangsu Province BE2023781Suzhou Science and Technology Project SJC2023005
6 · The paper itself

Abstract

DNA methylation plays a crucial role in human diseases pathogenesis. Substantial experimental evidence from clinical and biological studies has confirmed numerous methylation-disease associations, which provide valuable prior knowledge for advancing precision medicine through biomarker discovery and disease subtyping. To systematically mine reliable methylation prior knowledge from known DNA methylation-disease associations and develop robust computational methods for precision medicine applications, we propose MethPriorGCN. By integrating layer attention mechanisms and feature weighting mechanisms, MethPriorGCN not only identified reliable methylation digital biomarkers but also achieved superior disease subtype classification accuracy.

Indexed as

Computational BiologyDeep LearningDNA MethylationPrecision MedicineBiomarkersHumansBiomarkersbiomarker discoverydisease classificationDNA methylationfeature weightinggraph convolutional network

Identifiers

PMID40131311
PMCPMC11934576

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.