{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T00:04:59Z","timestamp":1765670699171,"version":"3.48.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China Projects","doi-asserted-by":"crossref","award":["12231018"],"award-info":[{"award-number":["12231018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Interdisciplinary Research Program of Ningxia Medical University","award":["JCXK2025003"],"award-info":[{"award-number":["JCXK2025003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Alzheimer\u2019s disease (AD) is a neurodegenerative disorder characterized by memory loss and cognitive decline. While graph convolutional networks (GCNs) have emerged as popular tools for AD diagnosis due to their ability to handle structural information and fuse multi-modal features, deep learning approaches face significant challenges including the requirement for large datasets and sensitivity to unbalanced label distributions in AD research. To address these limitations and enhance the flexibility of GCNs, we propose a graph convolutional network based on the meta-learning paradigm (ADMGCN) for early AD diagnosis. This approach incorporates weighting and dimensionality reduction to improve performance, storage, and training efficiency. By leveraging meta-learning, we sample subjects to create numerous label-balanced tasks, maximizing data utilization and mitigating the impact of label imbalance. Additionally, the meta-learning framework enables rapid adaptation to new tasks and facilitates independent testing of the GCN.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our model, ADMGCN, was extensively validated on the Alzheimer\u2019s Disease Neuroimaging Initiative datasets. It achieved a maximum accuracy of 73.7% in the multi-classification task for early AD diagnosis. In three binary classification tasks, the model also demonstrated strong performance, achieving accuracies of 92.8%, 88.0%, and 79.6%, respectively. These results confirm that the proposed method provides an effective approach and worthwhile support for the early diagnosis of Alzheimer\u2019s disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>ADMGCN is freely available at https:\/\/github.com\/WendySun16\/ADMGCN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf580","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T12:38:23Z","timestamp":1761568703000},"source":"Crossref","is-referenced-by-count":0,"title":["ADMGCN: graph convolutional network for Alzheimer\u2019s disease diagnosis with a meta-learning paradigm"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3970-6456","authenticated-orcid":false,"given":"Xiaowen","family":"Sun","sequence":"first","affiliation":[{"name":"College of Medical Information and Engineering, Ningxia Medical University , Yinchuan 750004,","place":["China"]},{"name":"Research Center for Mathematics and Interdisciplinary Sciences (Ministry of Education Frontiers Science Center for Nonlinear Expectations), Shandong University , Qingdao 266237,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8850-7833","authenticated-orcid":false,"given":"Jiahao","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center for Mathematics and Interdisciplinary Sciences (Ministry of Education Frontiers Science Center for Nonlinear Expectations), Shandong University , Qingdao 266237,","place":["China"]}]},{"given":"Guiying","family":"Yan","sequence":"additional","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing,","place":["China"]},{"name":"School of Mathematical Sciences, University of Chinese Academy of Sciences , Beijing 100190,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4761-6526","authenticated-orcid":false,"given":"Renmin","family":"Han","sequence":"additional","affiliation":[{"name":"College of Medical Information and Engineering, Ningxia Medical University , Yinchuan 750004,","place":["China"]},{"name":"Research Center for Mathematics and Interdisciplinary Sciences (Ministry of Education Frontiers Science Center for Nonlinear Expectations), Shandong University , Qingdao 266237,","place":["China"]},{"name":"Syneron Opal , 10281, Cayman","place":["Island"]}]}],"member":"286","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"volume":"19","key":"2025121319014088700_btaf580-B1","first-page":"1598"},{"key":"2025121319014088700_btaf580-B2","first-page":"353","article-title":"Autoencoders","author":"Bank","year":"2023","journal-title":"Mach Learn Data Sci Handb Data Mining Knowledge Discov 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