ADMGCN: graph convolutional network for Alzheimer’s disease diagnosis with a meta-learning paradigm

Authors

Jiahao Li (ORCID: https://orcid.org/0000-0003-1002-6573)
Renmin Han (ORCID: https://orcid.org/0000-0003-4761-6526)
Guiying Yan (ORCID: https://orcid.org/0000-0002-1736-5482)
Xiaowen Sun (ORCID: https://orcid.org/0009-0004-3191-5059)

Institutions

Chinese Academy of Sciences (CN)
Academy of Mathematics and Systems Science (CN)
SynZyme Technologies (United States) (US)
Ningxia Medical University (CN)
University of Chinese Academy of Sciences (CN)
Shandong University of Science and Technology (CN)

Publication Details

Journal: Bioinformatics
Published: 2025-10-27
DOI: https://doi.org/10.1093/bioinformatics/btaf580
Primary Topic: Dementia and Cognitive Impairment Research
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Natural Science Foundation of China

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article

ADMGCN: graph convolutional network for Alzheimer’s disease diagnosis with a meta-learning paradigm

Jiahao Li, Renmin Han, Guiying Yan, Xiaowen Sun

Oct 27, 2025

Bioinformatics

Dementia and Cognitive Impairment Research

article

ADMGCN: graph convolutional network for Alzheimer’s disease diagnosis with a meta-learning paradigm

Jiahao Li, Renmin Han, Guiying Yan, Xiaowen Sun

October 27, 2025article en

Abstract

No abstract available for this paper.

BioinformaticsVol. 41(12)

Chinese Academy of Sciences (CN), Academy of Mathematics and Systems Science (CN), SynZyme Technologies (United States) (US), Ningxia Medical University (CN), University of Chinese Academy of Sciences (CN), Shandong University of Science and Technology (CN)

National Natural Science Foundation of China

Openalex Percentile: Top 63%

Dementia and Cognitive Impairment Research

DOI Open Access PDF OpenAlex

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